By Nanmanat Disayakamonpan
Text Analysis and Natural Language Processing, Constructor University
1.1 Importing Libraries
1.2 Web Scraping for Extracting Data
1.3 Understanding the Data
1.4 Checking Missing Values
2.1 Exploratory Data Analysis (EDA)
2.2 Text Exploratory before Preprocessing Steps
2.3 Preprocessing Steps for Text Analysis
3. Main Analysis and Methodology
3.1 Text Mining after Preprocessing Steps
3.2 Topic Modeling
3.3 Clustering Classification
3.4 Employee Sentiment Analysis
I imported essential Python libraries for data analysis, natural language processing (NLP), machine learning, and visualization.
# Data Analysis:
import pandas as pd
# Numerical Computing/Mathematical Operations:
import numpy as np
# Data Visualization:
import matplotlib.pyplot as plt
import seaborn as sns
# Natural Language Processing (NLP):
import re # for regular expressions.
import textblob # for simple NLP tasks like sentiment analysis.
import nltk # for NLP tasks such as tokenization and part-of-speech tagging
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk import ngrams
import spacy # for efficient NLP processing and pre-trained models
import gensim # for topic modeling and document similarity
from gensim.corpora import Dictionary # for topic modeling
from gensim.models.ldamodel import LdaModel # for topic modeling
# Machine Learning:
import sklearn # for machine learning algorithms and evaluation metrics
# Other Utilities:
import warnings # for handling warning messages.
warnings.filterwarnings('ignore')
from bs4 import BeautifulSoup # for web scraping
from wordcloud import WordCloud # for word cloud generation
from transformers import pipeline # for Hugging Face's transformers.
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer # for converting text data into numerical features
from collections import Counter # for calculating the frequency of elements in a collection.
from sklearn.cluster import KMeans # for clustering
from sklearn.metrics import silhouette_score # for evaluation metrics
from spacy.lang.en import English # for tokenizing English text.
from spacy.lang.en.stop_words import STOP_WORDS # A set of common stop words are often filtered out during text processing tasks.
The qualitative data source for this study consists of employee reviews extracted by web scraping from the Glassdoor platform, representing two distinct entities within the financial sector: Deutsche Bank, a traditional banking institution, and N26, a prominent FinTech company.
# Define the file paths for the HTML files
html_files = [
'Deutsche_Bank_Reviews1.html',
'Deutsche_Bank_Reviews2.html',
'Deutsche_Bank_Reviews3.html',
'Deutsche_Bank_Reviews4.html',
'Deutsche_Bank_Reviews5.html',
'Deutsche_Bank_Reviews6.html',
'Deutsche_Bank_Reviews7.html',
'Deutsche_Bank_Reviews8.html',
'Deutsche_Bank_Reviews9.html',
'Deutsche_Bank_Reviews10.html',
'Deutsche_Bank_Reviews11.html',
'Deutsche_Bank_Reviews12.html',
'Deutsche_Bank_Reviews13.html',
'Deutsche_Bank_Reviews14.html',
'Deutsche_Bank_Reviews15.html',
'Deutsche_Bank_Reviews16.html',
'Deutsche_Bank_Reviews17.html',
'Deutsche_Bank_Reviews18.html',
'Deutsche_Bank_Reviews19.html',
'Deutsche_Bank_Reviews20.html'
]
# Function to extract data from a single HTML file
def extract_data_from_html(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
html_content = file.read()
soup = BeautifulSoup(html_content, 'html.parser')
employee_titles, locations, pros, cons, ratings = [], [], [], [], []
for tag in soup.find_all(class_="review-details_employee__MeSp3"):
employee_titles.append(tag.text.strip() if tag else "null")
for tag in soup.find_all(class_="review-details_location__xxMP7"):
locations.append(tag.text.strip() if tag else "null")
for tag in soup.select('span[data-test="review-text-pros"]'):
pros.append(tag.get_text(strip=True))
for tag in soup.select('span[data-test="review-text-cons"]'):
cons.append(tag.get_text(strip=True))
for tag in soup.find_all(class_="review-details_overallRating__Rxhdr"):
ratings.append(tag.text.strip() if tag else "null")
max_len = max(len(employee_titles), len(locations), len(pros), len(cons), len(ratings))
employee_titles += [None] * (max_len - len(employee_titles))
locations += [None] * (max_len - len(locations))
pros += [None] * (max_len - len(pros))
cons += [None] * (max_len - len(cons))
ratings += [None] * (max_len - len(ratings))
return pd.DataFrame({
"Employee Title": employee_titles,
"Location": locations,
"Pros": pros,
"Cons": cons,
"Rating": ratings
})
# Extract data from all HTML files and combine them
all_data_dfs = [extract_data_from_html(file) for file in html_files]
db = pd.concat(all_data_dfs, ignore_index=True)
db['Company Name'] = 'Deutsche Bank'
# Save the combined dataframe to a CSV file (appending to the existing one or creating a new one)
combined_csv_file_path = "DeutschBank_Reviews_combined.csv"
db.to_csv(combined_csv_file_path, index=False)
combined_csv_file_path, db.shape
('DeutschBank_Reviews_combined.csv', (200, 6))
# Define the file paths for the HTML files
html_files2 = [
'N26_Reviews1.html',
'N26_Reviews2.html',
'N26_Reviews3.html',
'N26_Reviews4.html',
'N26_Reviews5.html',
'N26_Reviews6.html',
'N26_Reviews7.html',
'N26_Reviews8.html',
'N26_Reviews9.html',
'N26_Reviews10.html',
'N26_Reviews11.html',
'N26_Reviews12.html',
'N26_Reviews13.html',
'N26_Reviews14.html',
'N26_Reviews15.html',
'N26_Reviews16.html',
'N26_Reviews17.html',
'N26_Reviews18.html',
'N26_Reviews19.html',
'N26_Reviews20.html'
]
# Function to extract data from a single HTML file
def extract_data_from_html(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
html_content = file.read()
soup = BeautifulSoup(html_content, 'html.parser')
employee_titles, locations, pros, cons, ratings = [], [], [], [], []
for tag in soup.find_all(class_="review-details_employee__MeSp3"):
employee_titles.append(tag.text.strip() if tag else "null")
for tag in soup.find_all(class_="review-details_location__xxMP7"):
locations.append(tag.text.strip() if tag else "null")
for tag in soup.select('span[data-test="review-text-pros"]'):
pros.append(tag.get_text(strip=True))
for tag in soup.select('span[data-test="review-text-cons"]'):
cons.append(tag.get_text(strip=True))
for tag in soup.find_all(class_="review-details_overallRating__Rxhdr"):
ratings.append(tag.text.strip() if tag else "null")
max_len = max(len(employee_titles), len(locations), len(pros), len(cons), len(ratings))
employee_titles += [None] * (max_len - len(employee_titles))
locations += [None] * (max_len - len(locations))
pros += [None] * (max_len - len(pros))
cons += [None] * (max_len - len(cons))
ratings += [None] * (max_len - len(ratings))
return pd.DataFrame({
"Employee Title": employee_titles,
"Location": locations,
"Pros": pros,
"Cons": cons,
"Rating": ratings
})
# Extract data from all HTML files and combine them
all_data_dfs2 = [extract_data_from_html(file) for file in html_files2]
n26 = pd.concat(all_data_dfs2, ignore_index=True)
n26['Company Name'] = 'N26'
# Save the combined dataframe to a CSV file (appending to the existing one or creating a new one)
combined_csv_file_path = "N26_Reviews_combined.csv"
n26.to_csv(combined_csv_file_path, index=False)
combined_csv_file_path, n26.shape
('N26_Reviews_combined.csv', (200, 6))
There are two datasets (“db” and “n26”) which are job reviews from Deutsche Bank and N26. Each dataset has 200 observations, ensuring a balanced representation to mitigate potential biases arising from dataset size discrepancies.
Each dataset has six features, including five categorical variables: Employee Title, Location, Pros, Cons, and Company Name. Additionally, a numerical variable is the Rating provided by employees. The "Pros" and "Cons" columns consist of positive and negative sentiments based on the respective organizations' work environments, facilitating comparative analysis between Deutsche Bank and N26 across various dimensions such as work-life balance, compensation, organizational culture, and more.
I intend to analyze only the most recent Glassdoor reviews to ensure that my findings reflect the organization's current status. Core sampling characteristics include the review's sentiment, specific mentions of organizational aspects, and overall employee satisfaction.
Below are the feature names of both datasets along with their descriptions.
Table 1: The feature names of both datasets along with their descriptions.
| Variable Name | Description | Values |
|---|---|---|
| Employee Title | Employee's Job Title/Position | Categorical: e.g. Sales Manager/ Senior Analyst/ Business Analyst/ Anonymous Employee |
| Location | Office Location in Germany (City, Region) | Cetegorical: e.g. Munich, Bavaria/ Franfurt am Main/ Berlin |
| Pros | Positive Reviews | Categorical: e.g. Good pay and fair working hours |
| Cons | Negative Reviews | Categorical: e.g. Old systems and lack of flexibility |
| Rating | Company's Ratings from Employees | Numeric: from 1 to 5 |
| Company Name | Tradition Bank/Fintech Company | Cetegorical: “Deutsche Bank” or “N26” |
db.head(10)
| Employee Title | Location | Pros | Cons | Rating | Company Name | |
|---|---|---|---|---|---|---|
| 0 | Compliance Analyst | Berlin | The environment within the team was open and n... | The temporary contract and the salary | 5.0 | Deutsche Bank |
| 1 | Vice President M&A | Frankfurt am Main | Deutsche Bank in Germany still kind of #1, and... | Globally lacking behind BB US banks, but keeps... | 5.0 | Deutsche Bank |
| 2 | Graduate Trainee In Technology/Digital | Frankfurt am Main | The job stands for a very good work-life balance. | Less challenging for career beginners. | 4.0 | Deutsche Bank |
| 3 | Internship | Frankfurt am Main | Flexible work and 39h/ week | In Corona times difficult to network | 4.0 | Deutsche Bank |
| 4 | Corporate Treasury Sales | Frankfurt am Main | Trading floor experience and good socials | Monotone work and no good training | 4.0 | Deutsche Bank |
| 5 | Risk Analyst | Berlin | Flexible working hours, good work-life balance | There is not many Career Development Opportuni... | 4.0 | Deutsche Bank |
| 6 | Bank Assistant | Hamburg | Helpful for everyone job seeker | i dont know anything about it thank you so much | 4.0 | Deutsche Bank |
| 7 | Analyst | Frankfurt am Main | Friendly and helpful environment, good payment | Heavy workload sometimes 100h weeks \nLittle t... | 4.0 | Deutsche Bank |
| 8 | CIB Operation Regional Lead | Frankfurt am Main | Work Life balance, Pay is okay | Internal promotion is difficult Politics | 4.0 | Deutsche Bank |
| 9 | Internship | Frankfurt am Main | Facilities, relaxed working hours, opportunity... | Responsibility, a lot of admin work | 2.0 | Deutsche Bank |
print(db.describe())
Employee Title Location \
count 200 200
unique 125 15
top Intern Berlin
freq 12 92
Pros \
count 200
unique 200
top The environment within the team was open and n...
freq 1
Cons Rating Company Name
count 200 200 200
unique 200 5 1
top The temporary contract and the salary 4.0 Deutsche Bank
freq 1 95 200
n26.head(10)
| Employee Title | Location | Pros | Cons | Rating | Company Name | |
|---|---|---|---|---|---|---|
| 0 | Anonymous Employee | Berlin | Good teams and setups, solid Ways of working | Management crisis, high turnover on management... | 3.0 | N26 |
| 1 | Senior Software Engineer | Berlin | Work environment relaxed, even better after th... | Management is not really ready to listen opini... | 2.0 | N26 |
| 2 | Anonymous Employee | Berlin | The teams have some of the nicest and hard wor... | The managers in this company do not support th... | 1.0 | N26 |
| 3 | User Researcher | Berlin | Diverse work, lot's of in-house talent | Some uncertainty with organisation direction | 4.0 | N26 |
| 4 | Analytics Engineer | Berlin | Wonderful people to work with and state of the... | Project workflow can be lost in the thread whe... | 3.0 | N26 |
| 5 | International Customer Support Specialist | Berlin | Good place to work in general | Not so much chance of growth | 3.0 | N26 |
| 6 | Compliance Specialist | Berlin | the company still is quite interesting | the salary is quite low | 3.0 | N26 |
| 7 | Product Designer | Berlin | Great people. Fun product. Learnt so much | Too many layers in the org after scaling a lot... | 5.0 | N26 |
| 8 | Backend Engineer | Berlin | good vibes\ngood peoples\nnice office in the c... | problems with budget regarding salary updates | 4.0 | N26 |
| 9 | Senior Product Analyst | Berlin | Full Remote work. The product analytics team i... | Product leaders need more focus on being more ... | 3.0 | N26 |
print(n26.describe())
Employee Title Location \
count 200 200
unique 129 1
top Anonymous Employee Berlin
freq 21 200
Pros \
count 200
unique 200
top Good teams and setups, solid Ways of working
freq 1
Cons Rating Company Name
count 200 200 200
unique 200 5 1
top Management crisis, high turnover on management... 4.0 N26
freq 1 56 200
After creating two dataframes, I checked each dataset to identify missing values. This information is crucial for understanding the completeness of the dataset and determining any necessary data cleaning or imputation steps before analysis. From the output, I observed that both DataFrames have none of missing values in any column.
# Check for missing values in the db DataFrame
db_missing_values = db.isnull().sum()
print("Missing values in db DataFrame:")
print(db_missing_values)
# Check for missing values in the n26 DataFrame
n26_missing_values = n26.isnull().sum()
print("\nMissing values in n26 DataFrame:")
print(n26_missing_values)
Missing values in db DataFrame: Employee Title 0 Location 0 Pros 0 Cons 0 Rating 0 Company Name 0 dtype: int64 Missing values in n26 DataFrame: Employee Title 0 Location 0 Pros 0 Cons 0 Rating 0 Company Name 0 dtype: int64
First, I would like to compare the distribution of office locations by the number of employees for Deutsche Bank and N26, respectively, by creating a bar graph for each company.
db_location = db['Location'].value_counts().sort_index()
n26_location = n26['Location'].value_counts().sort_index()
# Create a figure with the desired size
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 6)) # Adjust the figsize as needed
# Deutsche Bank
db_location.sort_values().plot(kind='barh', color='darkblue', ax=axes[0])
axes[0].set_title('Location Distribution for Deutsche Bank')
axes[0].set_xlabel('Number of Employees')
axes[0].set_ylabel('Office Location')
# N26
n26_location.sort_values().plot(kind='barh', color='mediumseagreen', ax=axes[1])
axes[1].set_title('Location Distribution for N26')
axes[1].set_xlabel('Number of Employees')
axes[1].set_ylabel('Office Location')
plt.tight_layout()
plt.show()
On the left, the Deutsche Bank graph has multiple locations, with Berlin having the highest number of employees. Other cities, such as Frankfurt am Main, Eschborn, and Munich, also have significant numbers of employees but fewer than Berlin.
The graphs visually represent that Deutsche Bank has a broader distribution of office locations throughout Germany, while N26 appears to be highly centralized in Berlin.
Secondly, I used pie charts to represent the top 10 employee job titles/positions at Deutsche Bank and N26.
db_emptitle = db['Employee Title'].value_counts().sort_index()
n26_emptitle = n26['Employee Title'].value_counts().sort_index()
# Define custom color palettes for each pie chart
db_colors = plt.cm.Blues(np.linspace(0.2, 1, 10)) # Shades of blue for Deutsche Bank
n26_colors = plt.cm.Greens(np.linspace(0.2, 1, 10)) # Shades of green for N26
# Plot the pie charts with custom color palettes
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))
# Deutsche Bank
db_emptitle.sort_values(ascending=False).head(10).plot(kind='pie', ax=axes[0], autopct='%1.1f%%', colors=db_colors)
axes[0].set_ylabel('')
axes[0].set_title('Top 10 Employee Titles at Deutsche Bank')
# N26
n26_emptitle.sort_values(ascending=False).head(10).plot(kind='pie', ax=axes[1], autopct='%1.1f%%', colors=n26_colors)
axes[1].set_ylabel('')
axes[1].set_title('Top 10 Employee Titles at N26')
plt.tight_layout()
plt.show()
For Deutsche Bank (left chart, shaded in blues), the largest segment is 'Intern', making up 16.7% of the top positions and the second largest is 'Vice President', with 15.3%. Additionally, 'Software Engineer' and 'Anonymous Employee' accounts for 12.5% and 9.7%, respectively.
For N26 (right chart, shaded in greens), 'Anonymous Employee' dominates the chart with a significant 36.2% and 'Software Engineer' is also notable at 10.3%. In addition, ‘Recruiter’ and ‘Intern’ follow the same proportion, for 8.6%.
For the Rating column, I created two bar charts to display the distribution of ratings for Deutsche Bank and N26.
# Create subplots with 1 row and 2 columns
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(25, 8))
# Plotting ratings distribution of Deutsche Bank
db_ratings = db['Rating'].value_counts().sort_index()
total_db_reviews = db_ratings.sum()
db_ratings_percent = db_ratings / total_db_reviews * 100
db_ratings.plot(kind='bar', color='darkblue', ax=axes[0])
axes[0].set_title('Ratings Distribution of Deutsche Bank')
axes[0].set_xlabel('Rating for Deutsche Bank')
axes[0].set_ylabel('Number of Reviews')
axes[0].tick_params(axis='x', rotation=0)
# Annotate each bar with its value for Deutsche Bank
for i, (value, percent) in enumerate(zip(db_ratings, db_ratings_percent)):
axes[0].text(i, value, f'{value}\n({percent:.1f}%)', ha='center', va='bottom')
# Plotting ratings distribution of N26
n26_ratings = n26['Rating'].value_counts().sort_index()
total_n26_reviews = n26_ratings.sum()
n26_ratings_percent = n26_ratings / total_n26_reviews * 100
n26_ratings.plot(kind='bar', color='mediumseagreen', ax=axes[1])
axes[1].set_title('Ratings Distribution of N26')
axes[1].set_xlabel('Rating for N26')
axes[1].set_ylabel('Number of Reviews')
axes[1].tick_params(axis='x', rotation=0)
# Annotate each bar with its value for N26
for i, (value, percent) in enumerate(zip(n26_ratings, n26_ratings_percent)):
axes[1].text(i, value, f'{value}\n({percent:.1f}%)', ha='center', va='bottom')
plt.tight_layout()
plt.show()
For Deutsche Bank, the chart shows that the majority of reviews are very positive, with 95 (~47.5%) and 55 reviews (~27.5%) at a 4 and 5-star rating, respectively. Meanwhile, the most common ratin of N26 is 4 stars, with 28% or 56 reviews. Both 5-star and 3-star ratings have 46 reviews or 23% each. Interestingly, both companies have a higher concentration of 4-star and 5-star ratings, indicating generally positive feedback.
For this step, I would like to show the total word count in the 'Pros' and 'Cons' reviews for Deutsche Bank and N26 before preprocessing. It splits each review into words and sums up the word counts for both 'Pros' and 'Cons' separately for each company.
# Total word count in 'Pros' and 'Cons'
db_wordcount_pros = db['Pros'].apply(lambda x: len(x.split())).sum()
db_wordcount_cons = db['Cons'].apply(lambda x: len(x.split())).sum()
n26_wordcount_pros = n26['Pros'].apply(lambda x: len(x.split())).sum()
n26_wordcount_cons = n26['Cons'].apply(lambda x: len(x.split())).sum()
print(f"Total word count in 'Pros' reviews of Deutsche Bank before preprocessing: {db_wordcount_pros} words")
print(f"Total word count in 'Cons' reviews of Deutsche Bank before preprocessing: {db_wordcount_cons} words")
print(f"Total word count in 'Pros' reviews of N26 before preprocessing: {n26_wordcount_pros} words")
print(f"Total word count in 'Cons' reviews of N26 before preprocessing: {n26_wordcount_cons} words")
Total word count in 'Pros' reviews of Deutsche Bank before preprocessing: 1936 words Total word count in 'Cons' reviews of Deutsche Bank before preprocessing: 2416 words Total word count in 'Pros' reviews of N26 before preprocessing: 3776 words Total word count in 'Cons' reviews of N26 before preprocessing: 4715 words
The output indicates that before preprocessing, there are 1936 words in the 'Pros' reviews and 2416 words in the 'Cons' reviews for Deutsche Bank, while for N26, there are 3776 words in the 'Pros' reviews and 4715 words in the 'Cons' reviews.
This step computes the mean length of each review by applying the len() function to each review's text and then the output shows the average length of 'Pros' and 'Cons' reviews, measured in terms of the number of characters, for both Deutsche Bank and N26 before preprocessing.
# Mean length of reviews (in terms of number of characters)
db_meanlength_pros = db['Pros'].apply(len).mean()
db_meanlength_cons = db['Cons'].apply(len).mean()
n26_meanlength_pros = n26['Pros'].apply(len).mean()
n26_meanlength_cons = n26['Cons'].apply(len).mean()
print(f"Average length of 'Pros' reviews of Deutsche Bank before preprocessing: {db_meanlength_pros} characters")
print(f"Average length of 'Cons' reviews of Deutsche Bank before preprocessing: {db_meanlength_cons} characters")
print(f"Average length of 'Pros' reviews of N26 before preprocessing: {n26_meanlength_pros} characters")
print(f"Average length of 'Cons' reviews of N26 before preprocessing: {n26_meanlength_cons} characters")
Average length of 'Pros' reviews of Deutsche Bank before preprocessing: 62.215 characters Average length of 'Cons' reviews of Deutsche Bank before preprocessing: 73.71 characters Average length of 'Pros' reviews of N26 before preprocessing: 113.13 characters Average length of 'Cons' reviews of N26 before preprocessing: 142.635 characters
The results show that, on average, 'Pros' and 'Cons' reviews for Deutsche Bank have a length of approximately 61.59 characters and 74.06 characters, respectively, before preprocessing. For N26, the average length of 'Pros' and 'Cons' reviews is approximately 113.13 characters and 142.635 characters, respectively, before preprocessing.
Before preprocessing, I calculated the average word length in 'Pros' and 'Cons' reviews for both Deutsche Bank and N26. It computes the mean word length for each review by iterating over each word in the review, calculating the length of each word, and then averaging the lengths.
# Average word length in 'Pros' and 'Cons'
db_avg_wordlength_pros = db['Pros'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
db_avg_wordlength_cons = db['Cons'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
n26_avg_wordlength_pros = n26['Pros'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
n26_avg_wordlength_cons = n26['Cons'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
print(f"Average word length in 'Pros' reviews of Deutsche Bank before preprocessing: {db_avg_wordlength_pros}")
print(f"Average word length in 'Cons' reviews of Deutsche Bank before preprocessing: {db_avg_wordlength_cons}")
print(f"Average word length in 'Pros' reviews of N26 before preprocessing: {n26_avg_wordlength_pros}")
print(f"Average word length in 'Cons' reviews of N26 before preprocessing: {n26_avg_wordlength_cons}")
Average word length in 'Pros' reviews of Deutsche Bank before preprocessing: 5.459111745372114 Average word length in 'Cons' reviews of Deutsche Bank before preprocessing: 5.0006024012057795 Average word length in 'Pros' reviews of N26 before preprocessing: 5.188589655272812 Average word length in 'Cons' reviews of N26 before preprocessing: 5.070385461326041
The results indicate that, on average, words in 'Pros' reviews for Deutsche Bank have a length of approximately 5.46 characters, while words in 'Cons' reviews have a length of approximately 5.00 characters before preprocessing. For N26, the average word length in 'Pros' reviews is approximately 5.19 characters, and in 'Cons' reviews, it is approximately 5.07 characters before preprocessing.
This step identifies the most common words in 'Pros' and 'Cons' reviews for both Deutsche Bank and N26 before preprocessing. It counts the occurrences of each word in the text of all 'Pros' and 'Cons' reviews and retrieve the top 10 most common words and their frequencies.
# Common words in 'Pros' and 'Cons' reviews of Deutsche Bank before preprocessing
db_commonwords_pros = Counter(" ".join(db['Pros']).split()).most_common(10)
db_commonwords_cons = Counter(" ".join(db['Cons']).split()).most_common(10)
print("Common words in 'Pros' reviews of Deutsche Bank before preprocessing:")
print(db_commonwords_pros)
print("\nCommon words in 'Cons' reviews of Deutsche Bank before preprocessing:")
print(db_commonwords_cons)
Common words in 'Pros' reviews of Deutsche Bank before preprocessing:
[('and', 71), ('good', 46), ('work', 41), ('-', 40), ('of', 37), ('Good', 36), ('to', 35), ('a', 32), ('balance', 28), ('the', 26)]
Common words in 'Cons' reviews of Deutsche Bank before preprocessing:
[('to', 77), ('and', 65), ('the', 47), ('a', 43), ('of', 37), ('in', 29), ('for', 28), ('is', 26), ('not', 26), ('you', 26)]
# Common words in 'Pros' and 'Cons' reviews of N26 before preprocessing
n26_commonwords_pros = Counter(" ".join(n26['Pros']).split()).most_common(10)
n26_commonwords_cons = Counter(" ".join(n26['Cons']).split()).most_common(10)
print("Common words in 'Pros' reviews of N26 before preprocessing:")
print(n26_commonwords_pros)
print("\nCommon words in 'Cons' reviews of N26 before preprocessing:")
print(n26_commonwords_cons)
Common words in 'Pros' reviews of N26 before preprocessing:
[('and', 142), ('to', 117), ('-', 100), ('the', 99), ('a', 76), ('of', 71), ('work', 49), ('is', 45), ('for', 42), ('in', 41)]
Common words in 'Cons' reviews of N26 before preprocessing:
[('to', 157), ('and', 131), ('the', 130), ('of', 100), ('a', 87), ('-', 80), ('is', 79), ('are', 63), ('not', 58), ('in', 55)]
For 'Pros' reviews of Deutsche Bank, common words include 'and', 'good', '-', and 'work'. On the other hand, for 'Cons' reviews, common words include 'to', 'and', and 'the'. Meanwhile 'Pros' reviews of N26, common words include 'and', 'to', '-' and 'the'. In contrast, for 'Cons' reviews, common words include 'to', 'and', and 'the'.
For this process, I defined a function to processes text data by using the spaCy library and loaded a pre-trained spaCy model for English language (en_core_web_md). The function is then applied to the 'Pros' and 'Cons' entries of the Deutsche Bank and N26 datasets. It then tokenizes the text, performs part-of-speech (POS) tagging on each token, and identifies any named entities found in the text using Named Entity Recognition (NER).
This process provides linguistic analysis of the text data, helping to understand the grammatical structure, identify important words and phrases, and recognize named entities such as organizations, people, and locations.
# Load the pre-trained spaCy model
nlp = spacy.load('en_core_web_md')
def process_text_with_spacy(text):
# Process the text with spaCy
doc = nlp(text)
# Tokenization and POS tagging
print("Tokenization and POS Tagging:")
for token in doc:
print(f"{token.text:{12}} {token.pos_:{10}}")
# Checking if there are named entities before printing
if doc.ents:
print("\nNamed Entity Recognition:")
for ent in doc.ents:
print(f"{ent.text:{17}} {ent.label_}")
print("\n" + "-"*50 + "\n")
# Apply the function to the first 5 'Pros' entries
print("Processing 'Pros':")
for db_pros_text in db['Pros']:
process_text_with_spacy(db_pros_text)
# Apply the function to the first 5 'Cons' entries
print("Processing 'Cons':")
for db_cons_text in db['Cons']:
process_text_with_spacy(db_cons_text)
Processing 'Pros':
Tokenization and POS Tagging:
The DET
environment NOUN
within ADP
the DET
team NOUN
was AUX
open ADJ
and CCONJ
not PART
competitive ADJ
and CCONJ
people NOUN
were AUX
kind ADJ
to ADP
each DET
other ADJ
SPACE
The DET
manager NOUN
of ADP
the DET
team NOUN
was AUX
also ADV
very ADV
nice ADJ
and CCONJ
a DET
great ADJ
leader NOUN
overall NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Deutsche PROPN
Bank PROPN
in ADP
Germany PROPN
still ADV
kind ADV
of ADV
# SYM
1 NUM
, PUNCT
and CCONJ
involved VERB
in ADP
all DET
big ADJ
name NOUN
deals NOUN
Named Entity Recognition:
Deutsche Bank ORG
Germany GPE
1 MONEY
--------------------------------------------------
Tokenization and POS Tagging:
The DET
job NOUN
stands VERB
for ADP
a DET
very ADV
good ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Flexible ADJ
work NOUN
and CCONJ
39h/ NUM
week NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Trading NOUN
floor NOUN
experience NOUN
and CCONJ
good ADJ
socials NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Flexible ADJ
working NOUN
hours NOUN
, PUNCT
good ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
Named Entity Recognition:
Flexible working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
Helpful ADJ
for ADP
everyone PRON
job NOUN
seeker NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Friendly ADJ
and CCONJ
helpful ADJ
environment NOUN
, PUNCT
good ADJ
payment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Work PROPN
Life PROPN
balance NOUN
, PUNCT
Pay NOUN
is AUX
okay ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Facilities NOUN
, PUNCT
relaxed ADJ
working NOUN
hours NOUN
, PUNCT
opportunity NOUN
to PART
look VERB
at ADP
how SCONJ
other ADJ
teams NOUN
work VERB
--------------------------------------------------
Tokenization and POS Tagging:
Amazing ADJ
worklife NOUN
balance NOUN
and CCONJ
flexibility NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
international ADJ
team NOUN
SPACE
- PUNCT
great ADJ
guidance NOUN
and CCONJ
managers NOUN
SPACE
- PUNCT
interesting ADJ
problems NOUN
and CCONJ
steep ADJ
learning NOUN
curve NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Stable ADJ
employment NOUN
. PUNCT
SPACE
Good ADJ
work NOUN
life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
nice ADJ
colleagues NOUN
, PUNCT
latest ADJ
IT NOUN
stack NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
access NOUN
to ADP
core NOUN
business NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Strong ADJ
russian ADJ
community NOUN
SPACE
Relocation PROPN
oppotrunity NOUN
Named Entity Recognition:
russian NORP
--------------------------------------------------
Tokenization and POS Tagging:
everything PRON
- PUNCT
good ADJ
pay NOUN
, PUNCT
flex ADJ
working NOUN
hours NOUN
, PUNCT
culture NOUN
, PUNCT
multi ADJ
national ADJ
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
managers NOUN
are AUX
keen ADJ
to ADP
networking NOUN
and CCONJ
knowledge NOUN
sharing VERB
--------------------------------------------------
Tokenization and POS Tagging:
As ADP
an DET
employer NOUN
it PRON
is AUX
fine ADJ
, PUNCT
provides VERB
loads NOUN
of ADP
security NOUN
and CCONJ
stability NOUN
. PUNCT
Many ADJ
talented ADJ
people NOUN
, PUNCT
also ADV
innovative ADJ
minds NOUN
. PUNCT
Diversity NOUN
is AUX
respected VERB
, PUNCT
I PRON
as ADP
a DET
young ADJ
female ADJ
employee NOUN
got VERB
loads NOUN
of ADP
mentoring VERB
and CCONJ
support NOUN
to PART
build VERB
a DET
carreer NOUN
. PUNCT
Never ADV
felt VERB
negatively ADV
discriminated VERB
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
work NOUN
and CCONJ
life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Experienced ADJ
coworkers NOUN
. PUNCT
Relatively ADV
modern ADJ
tech NOUN
stack NOUN
( PUNCT
if SCONJ
it PRON
's AUX
not PART
a DET
legacy NOUN
project NOUN
) PUNCT
. PUNCT
Work NOUN
- PUNCT
life NOUN
balance NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
special ADJ
. PUNCT
Good ADJ
people NOUN
there ADV
--------------------------------------------------
Tokenization and POS Tagging:
World PROPN
bank NOUN
with ADP
wide ADJ
global ADJ
network NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Relxad PROPN
atmosphere NOUN
, PUNCT
loyalty NOUN
amongst ADP
colleagues NOUN
Named Entity Recognition:
Relxad GPE
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
people NOUN
. PUNCT
SPACE
Great ADJ
campus NOUN
. PUNCT
SPACE
and CCONJ
great ADJ
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Organized ADJ
, PUNCT
stable ADJ
management NOUN
, PUNCT
lunch NOUN
benefits NOUN
--------------------------------------------------
Tokenization and POS Tagging:
work NOUN
life NOUN
balance NOUN
is AUX
good ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Interesting ADJ
product NOUN
and CCONJ
domain NOUN
, PUNCT
clear ADJ
business NOUN
goals NOUN
--------------------------------------------------
Tokenization and POS Tagging:
you PRON
can AUX
choose VERB
work NOUN
you PRON
do AUX
SPACE
can AUX
choose VERB
goals NOUN
SPACE
care NOUN
for ADP
health NOUN
and CCONJ
well ADV
bieng PROPN
Named Entity Recognition:
well bieng PERSON
--------------------------------------------------
Tokenization and POS Tagging:
Excellent ADJ
breadth NOUN
of ADP
work NOUN
due ADP
to ADP
the DET
bank NOUN
's PART
size NOUN
and CCONJ
global ADJ
footprint NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Helpful ADJ
, PUNCT
professional ADJ
, PUNCT
nice ADJ
, PUNCT
kind ADJ
, PUNCT
great ADJ
communication NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Was AUX
a DET
good ADJ
atmosphere NOUN
working VERB
there ADV
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
work NOUN
life NOUN
balance NOUN
at ADP
the DET
office NOUN
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
benefits NOUN
, PUNCT
good ADJ
support NOUN
( PUNCT
HR PROPN
, PUNCT
Technologies PROPN
) PUNCT
, PUNCT
good ADJ
work NOUN
- PUNCT
life NOUN
- PUNCT
balance NOUN
Named Entity Recognition:
Technologies ORG
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
pay NOUN
, PUNCT
work NOUN
expectations NOUN
, PUNCT
work NOUN
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Is AUX
a DET
massin PROPN
company NOUN
and CCONJ
it PRON
will AUX
be AUX
a DET
good ADJ
addition NOUN
to ADP
your PRON
resume NOUN
Named Entity Recognition:
massin NORP
--------------------------------------------------
Tokenization and POS Tagging:
High PROPN
Quality PROPN
Engineers PROPN
team NOUN
relocated VERB
from ADP
Russia PROPN
SPACE
Interesting ADJ
projects NOUN
SPACE
Deep PROPN
dive VERB
into ADP
technologies NOUN
Named Entity Recognition:
Russia GPE
Deep PRODUCT
--------------------------------------------------
Tokenization and POS Tagging:
Excellent ADJ
experience NOUN
and CCONJ
good ADJ
compensation NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
opportunity NOUN
to PART
build VERB
an DET
international ADJ
network NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Experience NOUN
SPACE
Life PROPN
balance NOUN
SPACE
Good ADJ
pay NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Prestige PROPN
is AUX
good ADJ
and CCONJ
everyone PRON
knows VERB
the DET
company NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
great ADJ
leaders NOUN
in ADP
the DET
technology NOUN
division NOUN
SPACE
- PUNCT
nice ADJ
colleagues NOUN
to PART
work VERB
with ADP
SPACE
- PUNCT
good ADJ
corporates ADJ
values NOUN
SPACE
- PUNCT
good ADJ
training NOUN
program NOUN
SPACE
- PUNCT
regular ADJ
feedback NOUN
from ADP
management NOUN
SPACE
- PUNCT
everyone PRON
treated VERB
with ADP
respect NOUN
- PUNCT
diversity NOUN
and CCONJ
inclusion NOUN
is AUX
at ADP
the DET
heart NOUN
of ADP
this DET
TDI PROPN
team NOUN
Named Entity Recognition:
TDI ORG
--------------------------------------------------
Tokenization and POS Tagging:
Hybrid ADJ
working NOUN
style NOUN
SPACE
Great ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
was AUX
lucky ADJ
to PART
become VERB
a DET
part NOUN
of ADP
very ADV
supportive ADJ
and CCONJ
friendly ADJ
team NOUN
. PUNCT
All DET
the DET
other ADJ
people NOUN
in ADP
other ADJ
teams NOUN
also ADV
seemed VERB
very ADV
reliable ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Best ADJ
of ADP
best ADJ
company NOUN
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
free ADJ
schedule NOUN
SPACE
- PUNCT
good ADJ
bonuses NOUN
SPACE
- PUNCT
enough ADJ
vacation NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
Experience PROPN
, PUNCT
Nice PROPN
Employees PROPN
, PUNCT
Good PROPN
Party PROPN
, PUNCT
Nice PROPN
afterwork NOUN
experience NOUN
Named Entity Recognition:
Nice Employees ORG
Good Party ORG
Nice GPE
--------------------------------------------------
Tokenization and POS Tagging:
Depending VERB
on ADP
the DET
team NOUN
and CCONJ
department NOUN
, PUNCT
you PRON
get VERB
to PART
be AUX
in ADP
a DET
nice ADJ
, PUNCT
easy ADV
- PUNCT
going VERB
company NOUN
. PUNCT
It PRON
easy ADJ
to PART
keep VERB
a DET
balance NOUN
private ADJ
- PUNCT
work NOUN
life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Nice ADJ
workplace NOUN
and CCONJ
a DET
professional ADJ
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
alles VERB
gut NOUN
, PUNCT
process NOUN
in ADP
place NOUN
, PUNCT
good ADJ
structure NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
employees NOUN
had VERB
nice ADJ
knowledge NOUN
and CCONJ
skills NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
People PROPN
and CCONJ
work NOUN
env NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
work NOUN
life NOUN
balance NOUN
and CCONJ
security NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Technically ADV
challenging ADJ
tasks NOUN
, PUNCT
big ADJ
scale NOUN
of ADP
the DET
systems NOUN
, PUNCT
a DET
lot NOUN
of ADP
interconnected VERB
services NOUN
and CCONJ
teams NOUN
that PRON
's AUX
not PART
so ADV
simple ADJ
to PART
organise VERB
against ADP
common ADJ
target NOUN
goals NOUN
--------------------------------------------------
Tokenization and POS Tagging:
They PRON
fulfill VERB
all DET
contract NOUN
obligations NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
great ADJ
culture NOUN
and CCONJ
internship NOUN
remuneration NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Interesting ADJ
tasks NOUN
Valuable ADJ
work NOUN
I PRON
lot NOUN
of ADP
financial ADJ
knowledge NOUN
, PUNCT
experienced VERB
colleagues NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Workload NOUN
is AUX
not PART
too ADV
high ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Colleagues NOUN
are AUX
nice ADJ
. PUNCT
Projects NOUN
are AUX
interesting ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
fairly ADV
normal ADJ
work NOUN
hours NOUN
9 NUM
- SYM
18 NUM
with ADP
flexibility NOUN
Named Entity Recognition:
9-18 DATE
--------------------------------------------------
Tokenization and POS Tagging:
International ADJ
community NOUN
, PUNCT
English PROPN
is AUX
primary ADJ
language NOUN
, PUNCT
a DET
lot NOUN
of ADP
projects NOUN
to PART
choose VERB
from ADP
. PUNCT
You PRON
can AUX
find VERB
cutting VERB
edge NOUN
technologies NOUN
and CCONJ
something PRON
back ADV
to ADP
70th ADJ
- PUNCT
whatever PRON
you PRON
want VERB
. PUNCT
Named Entity Recognition:
English LANGUAGE
70th ORDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
life NOUN
balance PROPN
Office PROPN
locations NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Pleasant ADJ
attitude NOUN
towards ADP
employees NOUN
, PUNCT
high ADJ
wages NOUN
. PUNCT
There PRON
is VERB
an DET
opportunity NOUN
for ADP
career NOUN
growth NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
project NOUN
was AUX
good ADJ
, PUNCT
the DET
people NOUN
are AUX
smart ADJ
, PUNCT
the DET
WLB PROPN
is AUX
nice ADJ
. PUNCT
Named Entity Recognition:
WLB PERSON
--------------------------------------------------
Tokenization and POS Tagging:
This PRON
is AUX
nice ADJ
place NOUN
to PART
work VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Open VERB
for ADP
ideas NOUN
, PUNCT
good ADJ
opportunities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Team NOUN
members NOUN
, PUNCT
HR PROPN
and CCONJ
even ADV
managers NOUN
were AUX
pretty ADV
proactive ADJ
and CCONJ
open ADJ
minded NOUN
. PUNCT
Named Entity Recognition:
HR ORG
--------------------------------------------------
Tokenization and POS Tagging:
Lot NOUN
of ADP
learning NOUN
and CCONJ
growth NOUN
options NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Open ADJ
culture NOUN
and CCONJ
decent ADJ
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
relaxed ADJ
environment NOUN
if SCONJ
wanted VERB
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
salary NOUN
- PUNCT
mobility NOUN
- PUNCT
you PRON
can AUX
switch VERB
your PRON
career NOUN
path NOUN
inside ADP
DB PROPN
- PUNCT
despite SCONJ
the DET
bureaucracy NOUN
it PRON
is AUX
possible ADJ
to PART
adapt VERB
new ADJ
technologies NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
support NOUN
by ADP
supervisors NOUN
in ADP
the DET
course NOUN
of ADP
development NOUN
opportunities NOUN
etc X
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
i PRON
liked VERB
the DET
salary NOUN
increases NOUN
--------------------------------------------------
Tokenization and POS Tagging:
A DET
lot NOUN
of ADP
experienced ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
international ADJ
working NOUN
environment NOUN
which PRON
provides VERB
many ADJ
interesting ADJ
career NOUN
opportunities NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Professionalism NOUN
of ADP
colleagues NOUN
, PUNCT
friendly ADJ
atmosphere NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
working VERB
hours NOUN
and CCONJ
number NOUN
of ADP
holidays NOUN
Named Entity Recognition:
working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
flexible ADJ
hours NOUN
and CCONJ
work NOUN
from ADP
home NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Since SCONJ
the DET
bank NOUN
is AUX
very ADV
big ADJ
there PRON
are VERB
a DET
lot NOUN
of ADP
opportunities NOUN
for ADP
internal ADJ
movements NOUN
between ADP
geographies NOUN
and CCONJ
between ADP
departments NOUN
and CCONJ
even ADV
between ADP
business NOUN
, PUNCT
tech NOUN
, PUNCT
control NOUN
functions NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Flexible ADJ
environment NOUN
and CCONJ
good ADJ
benefits NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Fair NOUN
salary NOUN
rotations NOUN
into ADP
many ADJ
divisions NOUN
insights NOUN
into ADP
different ADJ
departments NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
culture NOUN
is AUX
quite ADV
supportive ADJ
of ADP
employees NOUN
in ADP
maintaining VERB
a DET
work NOUN
- PUNCT
life NOUN
balance NOUN
, PUNCT
there PRON
's VERB
a DET
decent ADJ
hybrid ADJ
work NOUN
policy NOUN
and CCONJ
they PRON
supply VERB
a DET
laptop NOUN
and CCONJ
are AUX
keen ADJ
to PART
keep VERB
up ADP
on ADP
advancements NOUN
to PART
improve VERB
a DET
work NOUN
environment NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good PROPN
HR PROPN
benefits NOUN
such ADJ
as ADP
maternity NOUN
leave NOUN
, PUNCT
Bildungsurlaubs NOUN
, PUNCT
sick ADJ
leave NOUN
, PUNCT
children NOUN
sick ADJ
leave VERB
. PUNCT
etc X
International PROPN
environment NOUN
Internal ADJ
moving NOUN
Named Entity Recognition:
Bildungsurlaubs PERSON
International environment Internal ORG
--------------------------------------------------
Tokenization and POS Tagging:
Benfits NOUN
in ADP
plenty ADJ
high ADJ
salary NOUN
Named Entity Recognition:
Benfits GPE
--------------------------------------------------
Tokenization and POS Tagging:
Good PROPN
HR PROPN
support NOUN
and CCONJ
facilitied VERB
--------------------------------------------------
Tokenization and POS Tagging:
Home NOUN
office NOUN
is AUX
a DET
great ADJ
option NOUN
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
development NOUN
plan NOUN
for ADP
young ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
As SCONJ
mentioned VERB
people NOUN
are AUX
amazing ADJ
. PUNCT
Devs NOUN
open ADJ
space NOUN
feels VERB
like ADP
common ADJ
place NOUN
instead ADV
of ADP
people NOUN
forced VERB
to PART
work VERB
in ADP
one NUM
room NOUN
. PUNCT
Every DET
one NOUN
I PRON
talked VERB
to PART
are AUX
interesting ADJ
people NOUN
that PRON
are AUX
good ADJ
people NOUN
with ADP
( PUNCT
at ADP
least ADJ
) PUNCT
interest NOUN
and CCONJ
love NOUN
in ADP
what PRON
are AUX
they PRON
doing VERB
. PUNCT
Possibilities NOUN
for ADP
work NOUN
/ SYM
life NOUN
balance NOUN
if SCONJ
needed VERB
. PUNCT
As ADV
well ADV
as SCONJ
possibilities NOUN
for ADP
quick ADJ
career NOUN
climb NOUN
( PUNCT
tho PROPN
promotions NOUN
only ADV
once ADV
a DET
year NOUN
, PUNCT
but CCONJ
any DET
effort NOUN
is AUX
recognized VERB
) PUNCT
Named Entity Recognition:
Devs PERSON
one CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Nice ADJ
worklife NOUN
balance NOUN
, PUNCT
stable ADJ
, PUNCT
benefits NOUN
from ADP
company NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Space NOUN
for ADP
growth NOUN
, PUNCT
opportunities NOUN
, PUNCT
getting AUX
challenged VERB
, PUNCT
job NOUN
safety NOUN
, PUNCT
employee NOUN
benefits NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
atmosphere NOUN
Smart ADJ
colleagues NOUN
New ADJ
office NOUN
Named Entity Recognition:
Smart ORG
--------------------------------------------------
Tokenization and POS Tagging:
flexible ADJ
work NOUN
, PUNCT
great ADJ
benefits NOUN
, PUNCT
nice ADJ
colleagues NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Freedom NOUN
to PART
innovate VERB
. PUNCT
Safety NOUN
net NOUN
of ADP
a DET
company NOUN
Clear ADJ
job NOUN
progression NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good PROPN
Environment PROPN
, PUNCT
Top ADJ
notch ADJ
Management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Culture NOUN
, PUNCT
benefits NOUN
and CCONJ
annual ADJ
leave NOUN
entitlement NOUN
Named Entity Recognition:
annual DATE
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
life NOUN
balance NOUN
is AUX
good ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Nice ADJ
time NOUN
, PUNCT
working NOUN
hours NOUN
were AUX
ok ADJ
for ADP
M&A PROPN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
team NOUN
Good ADJ
work NOUN
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
I PRON
was AUX
constantly ADV
taken VERB
care NOUN
of ADP
. PUNCT
- PUNCT
Employees NOUN
answered VERB
all DET
my PRON
questions NOUN
with ADP
patience NOUN
. PUNCT
- PUNCT
Everyone PRON
was AUX
kind ADJ
and CCONJ
welcoming ADJ
. PUNCT
- PUNCT
I PRON
was AUX
allowed VERB
and CCONJ
wanted VERB
to PART
discover VERB
all DET
facets NOUN
of ADP
the DET
branch NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
company NOUN
, PUNCT
good ADJ
money NOUN
, PUNCT
pay VERB
good NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
Salary PROPN
, PUNCT
30 NUM
days NOUN
off ADV
Named Entity Recognition:
30 days DATE
--------------------------------------------------
Tokenization and POS Tagging:
Tech NOUN
company NOUN
, PUNCT
Stable ADJ
, PUNCT
good ADJ
IT NOUN
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Can AUX
have VERB
a DET
good ADJ
work NOUN
/ SYM
life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
team NOUN
and CCONJ
clear ADJ
development NOUN
process NOUN
--------------------------------------------------
Tokenization and POS Tagging:
everything PRON
was AUX
okay ADJ
, PUNCT
went VERB
well ADV
--------------------------------------------------
Tokenization and POS Tagging:
Coffee NOUN
, PUNCT
people NOUN
, PUNCT
chill NOUN
staff NOUN
, PUNCT
Thursdays PROPN
Named Entity Recognition:
Thursdays DATE
--------------------------------------------------
Tokenization and POS Tagging:
Colleagues NOUN
, PUNCT
Gehalt PROPN
, PUNCT
work NOUN
life NOUN
balance NOUN
Named Entity Recognition:
Gehalt PERSON
--------------------------------------------------
Tokenization and POS Tagging:
-Great ADJ
teams NOUN
-good VERB
bonus NOUN
-opportunity NOUN
to PART
learn VERB
--------------------------------------------------
Tokenization and POS Tagging:
salary NOUN
, PUNCT
work NOUN
life NOUN
balance NOUN
, PUNCT
benefits NOUN
, PUNCT
friendly ADJ
colleagues NOUN
--------------------------------------------------
Tokenization and POS Tagging:
No DET
pressure NOUN
, PUNCT
hybrid NOUN
, PUNCT
good ADJ
coworkers NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Nice PROPN
Office PROPN
in ADP
the DET
heart NOUN
of ADP
Frankfurt PROPN
, PUNCT
nice ADJ
colleagues NOUN
Named Entity Recognition:
Nice Office ORG
Frankfurt GPE
--------------------------------------------------
Tokenization and POS Tagging:
Stable ADJ
company NOUN
, PUNCT
good ADJ
office NOUN
, PUNCT
good ADJ
engineers NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
amount NOUN
of ADP
skilled ADJ
specialists NOUN
to PART
work VERB
with ADP
. PUNCT
One NUM
of ADP
the DET
best ADJ
colleagues NOUN
I PRON
've AUX
ever ADV
had VERB
. PUNCT
Decent ADJ
management NOUN
who PRON
values VERB
people NOUN
. PUNCT
Named Entity Recognition:
One CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Strong ADJ
brand NOUN
recognition NOUN
and CCONJ
learning NOUN
path NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
environment NOUN
, PUNCT
happy ADJ
team NOUN
, PUNCT
comfort NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Stable ADJ
, PUNCT
nice ADJ
people NOUN
, PUNCT
good ADJ
work NOUN
life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Employee NOUN
friendly ADJ
, PUNCT
Lots NOUN
of ADP
option NOUN
to PART
switch VERB
between ADP
the DET
career NOUN
oppurtunity NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Development NOUN
and CCONJ
career NOUN
opportunity NOUN
, PUNCT
company NOUN
benefits NOUN
, PUNCT
international ADJ
community NOUN
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
pay NOUN
, PUNCT
good ADJ
people NOUN
, PUNCT
good ADJ
food NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
culture NOUN
, PUNCT
good ADJ
work NOUN
. PUNCT
Good ADJ
projects NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Professional ADJ
and CCONJ
proactive ADJ
people NOUN
, PUNCT
company NOUN
politics NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Think VERB
about ADP
all DET
their PRON
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
, PUNCT
good ADJ
work NOUN
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Well INTJ
structured VERB
. PUNCT
Steep ADJ
learning NOUN
curve NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
supervision NOUN
! PUNCT
We PRON
had VERB
very ADV
packed ADJ
days NOUN
and CCONJ
learned VERB
a DET
lot NOUN
about ADP
different ADJ
departments NOUN
. PUNCT
Named Entity Recognition:
days DATE
--------------------------------------------------
Tokenization and POS Tagging:
Stability NOUN
, PUNCT
bonuses NOUN
, PUNCT
13 NUM
salary NOUN
, PUNCT
salary NOUN
above ADP
average NOUN
on ADP
the DET
market NOUN
, PUNCT
international ADJ
environment NOUN
, PUNCT
colleagues NOUN
, PUNCT
mature ADJ
audit NOUN
methodology NOUN
Named Entity Recognition:
13 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Team PROPN
, PUNCT
Projects PROPN
and CCONJ
Work PROPN
Life PROPN
Balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
intense ADJ
, PUNCT
too ADV
much ADJ
work NOUN
, PUNCT
no DET
lunch NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Fast ADJ
interview NOUN
process NOUN
Well INTJ
known VERB
clients NOUN
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
global ADJ
impact NOUN
* PUNCT
turn VERB
over ADP
of ADP
people NOUN
with ADP
broad ADJ
experiences NOUN
and CCONJ
solid ADJ
education NOUN
* PUNCT
robustness NOUN
and CCONJ
standardization NOUN
of ADP
processes NOUN
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
skilled ADJ
co NOUN
- NOUN
workers NOUN
* PUNCT
work NOUN
- PUNCT
life NOUN
balance NOUN
* NOUN
modern ADJ
tech NOUN
stack NOUN
if SCONJ
you PRON
are AUX
lucky ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
good ADJ
team NOUN
with ADP
a DET
lot NOUN
of ADP
knowledge NOUN
--------------------------------------------------
Tokenization and POS Tagging:
flexible ADJ
, PUNCT
lots NOUN
time NOUN
for ADP
family NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Good ADJ
pay NOUN
- PUNCT
No NOUN
micro NOUN
management NOUN
- PUNCT
International ADJ
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Solid ADJ
employer NOUN
, PUNCT
employees NOUN
have VERB
lots NOUN
of ADP
rights NOUN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
think VERB
this PRON
is AUX
protect VERB
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
less ADJ
pressure NOUN
on ADP
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
-Flexibility NOUN
and CCONJ
Mutual ADJ
- PUNCT
trust NOUN
working NOUN
environment NOUN
-Fair NOUN
payment NOUN
-Many ADJ
net ADJ
- PUNCT
working VERB
opportunities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
People NOUN
are AUX
always ADV
willing ADJ
to PART
help VERB
each DET
other ADJ
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
benefits NOUN
, PUNCT
flexible ADJ
working NOUN
hours NOUN
Named Entity Recognition:
hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
very ADV
relaxed ADJ
environment NOUN
and CCONJ
they PRON
do AUX
take VERB
care NOUN
of ADP
their PRON
workers NOUN
. PUNCT
above ADP
average ADJ
salary NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Ability NOUN
to PART
work VERB
on ADP
interesting ADJ
projects NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
people NOUN
, PUNCT
challenging VERB
projects NOUN
, PUNCT
good ADJ
mobility NOUN
options NOUN
--------------------------------------------------
Tokenization and POS Tagging:
help VERB
to PART
arrange VERB
a DET
blue ADJ
card NOUN
and CCONJ
rent VERB
an DET
apartment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Collaborative ADJ
and CCONJ
positive ADJ
environment NOUN
, PUNCT
sense NOUN
of ADP
working VERB
towards ADP
a DET
common ADJ
goal NOUN
--------------------------------------------------
Tokenization and POS Tagging:
nice ADJ
office NOUN
in ADP
great ADJ
location NOUN
, PUNCT
small ADJ
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great PROPN
Employer PROPN
, PUNCT
Good ADJ
culture NOUN
and CCONJ
Support PROPN
, PUNCT
SPACE
Good ADJ
opportunities NOUN
and CCONJ
different ADJ
projects NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
people NOUN
SPACE
Opportunities NOUN
for SCONJ
juniors NOUN
to PART
grow VERB
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Sehr PROPN
gute NOUN
Benefits NOUN
SPACE
- PUNCT
Sehr PROPN
gute NOUN
Kantine PROPN
SPACE
- PUNCT
Home NOUN
office NOUN
möglich VERB
SPACE
- PUNCT
Vermögenswirksame PROPN
Leistungen PROPN
Named Entity Recognition:
möglich
- Vermögenswirksame Leistungen PERSON
--------------------------------------------------
Tokenization and POS Tagging:
work NOUN
life NOUN
balance NOUN
SPACE
okay ADJ
- PUNCT
ish ADJ
benefits NOUN
SPACE
ample ADJ
opportunities NOUN
for ADP
internal ADJ
move NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
working VERB
atmosphere NOUN
was AUX
pretty ADV
good ADJ
as ADV
well ADV
as ADP
the DET
colleagues NOUN
. PUNCT
SPACE
Human PROPN
Resources PROPN
SPACE
Working PROPN
hours NOUN
Named Entity Recognition:
Human Resources
Working ORG
--------------------------------------------------
Tokenization and POS Tagging:
Strong ADJ
team NOUN
and CCONJ
deal NOUN
flow NOUN
, PUNCT
in ADP
particular ADJ
in ADP
German ADJ
home NOUN
market NOUN
Named Entity Recognition:
German NORP
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
atmosphere NOUN
. PUNCT
SPACE
Not PART
much ADJ
pressure NOUN
put VERB
on ADP
workers NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Large ADJ
international ADJ
company NOUN
, PUNCT
safe ADJ
job NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
place NOUN
to PART
work VERB
and CCONJ
flexible ADJ
culture NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
good ADJ
work NOUN
culture NOUN
and CCONJ
work NOUN
life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
corporate ADJ
bank NOUN
gives VERB
you PRON
a DET
comprehensive ADJ
insight NOUN
into ADP
banking NOUN
--------------------------------------------------
Tokenization and POS Tagging:
flexible ADJ
working NOUN
time NOUN
, PUNCT
stable ADJ
job NOUN
depending VERB
where SCONJ
you PRON
are AUX
placed VERB
--------------------------------------------------
Tokenization and POS Tagging:
Big ADJ
bank NOUN
, PUNCT
opportunity NOUN
to PART
learn VERB
an DET
build NOUN
up ADP
--------------------------------------------------
Tokenization and POS Tagging:
Life NOUN
work NOUN
balance NOUN
is AUX
really ADV
good ADJ
--------------------------------------------------
Tokenization and POS Tagging:
High ADJ
client NOUN
exposure NOUN
for ADP
an DET
intern NOUN
, PUNCT
intensive ADJ
work NOUN
environment NOUN
but CCONJ
still ADV
fun ADJ
, PUNCT
diverse ADJ
tasks NOUN
in ADP
my PRON
division NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Lots NOUN
of ADP
internal ADJ
opportunities NOUN
for ADP
growth NOUN
SPACE
- PUNCT
Good ADJ
internal ADJ
mobility NOUN
SPACE
- PUNCT
Great ADJ
colleagues NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
work NOUN
culture NOUN
and CCONJ
collegues NOUN
--------------------------------------------------
Tokenization and POS Tagging:
International ADJ
, PUNCT
nice ADJ
team NOUN
, PUNCT
big ADJ
brand NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
culture NOUN
, PUNCT
Fair NOUN
pay NOUN
, PUNCT
Supportive ADJ
management NOUN
, PUNCT
decent ADJ
work NOUN
life NOUN
balance NOUN
Named Entity Recognition:
Fair ORG
--------------------------------------------------
Tokenization and POS Tagging:
teamwork NOUN
, PUNCT
tools NOUN
, PUNCT
atmosphere NOUN
, PUNCT
client NOUN
exposure NOUN
, PUNCT
steep ADJ
learning NOUN
curve NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Flexible ADJ
working NOUN
hours NOUN
SPACE
Home NOUN
office NOUN
opportunities NOUN
SPACE
Great ADJ
tech NOUN
in ADP
offices NOUN
Named Entity Recognition:
Flexible working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
International ADJ
mobility NOUN
is AUX
easily ADV
possible ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
people NOUN
SPACE
Subsidized ADJ
cafeteria NOUN
SPACE
Decent ADJ
working VERB
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Sustainable ADJ
, PUNCT
risk NOUN
averse ADJ
, PUNCT
modern ADJ
, PUNCT
challenging VERB
--------------------------------------------------
Tokenization and POS Tagging:
Amazing ADJ
colleagues NOUN
. PUNCT
SPACE
Management NOUN
respects VERB
us PRON
. PUNCT
SPACE
Working PROPN
Council PROPN
represents VERB
us PRON
. PUNCT
SPACE
Work NOUN
- PUNCT
Life NOUN
balance NOUN
--- PUNCT
> PUNCT
top NOUN
Named Entity Recognition:
Working Council ORG
--------------------------------------------------
Tokenization and POS Tagging:
Yet CCONJ
to PART
discover VERB
them PRON
. PUNCT
I PRON
just ADV
started VERB
--------------------------------------------------
Tokenization and POS Tagging:
Macht PROPN
meistens VERB
Spaß PROPN
hier PROPN
zu X
arbeiten VERB
Named Entity Recognition:
Macht meistens Spaß hier zu arbeiten PERSON
--------------------------------------------------
Tokenization and POS Tagging:
30 NUM
days NOUN
vacation NOUN
, PUNCT
work NOUN
from ADP
home NOUN
2/5 NUM
days NOUN
Named Entity Recognition:
30 days DATE
2/5 days DATE
--------------------------------------------------
Tokenization and POS Tagging:
many ADJ
project NOUN
you PRON
can AUX
work VERB
on ADP
SPACE
diverse ADJ
kind NOUN
of ADP
work NOUN
SPACE
challenging VERB
--------------------------------------------------
Tokenization and POS Tagging:
Various ADJ
risks NOUN
issues NOUN
. PUNCT
Collaboration NOUN
between ADP
departments NOUN
can AUX
be AUX
achieved VERB
. PUNCT
Colleagues NOUN
from ADP
other ADJ
departments NOUN
are AUX
communicative ADJ
. PUNCT
People NOUN
generally ADV
are AUX
nice ADJ
and CCONJ
approachable ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Decent ADJ
flexibility NOUN
- PUNCT
work NOUN
/ SYM
life NOUN
balance NOUN
SPACE
Market NOUN
level NOUN
pay NOUN
SPACE
International ADJ
environment NOUN
with ADP
many ADJ
interesting ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Opportunity NOUN
for ADP
learning VERB
a DET
lot NOUN
of ADP
areas NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
Relaxed ADJ
working NOUN
environment NOUN
and CCONJ
good ADJ
managers NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
you PRON
have VERB
many ADJ
many ADJ
challenges NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Average ADJ
work NOUN
life NOUN
balance NOUN
. PUNCT
SPACE
Decent ADJ
salary NOUN
SPACE
Brand NOUN
value NOUN
Named Entity Recognition:
Brand ORG
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
income NOUN
, PUNCT
good ADJ
reputation NOUN
, PUNCT
good ADJ
benefits NOUN
--------------------------------------------------
Tokenization and POS Tagging:
secure ADJ
job NOUN
with ADP
little ADJ
risk NOUN
of ADP
being AUX
sacked VERB
( PUNCT
or CCONJ
at ADP
least ADJ
you PRON
get VERB
sewerance NOUN
package NOUN
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
pay NOUN
, PUNCT
fantastic ADJ
working NOUN
environment NOUN
, PUNCT
... PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
WL NOUN
balance NOUN
SPACE
- PUNCT
Benefits NOUN
SPACE
- PUNCT
Cantine ADJ
SPACE
- PUNCT
Collegues PROPN
--------------------------------------------------
Tokenization and POS Tagging:
Money NOUN
home NOUN
office NOUN
work NOUN
time NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Team NOUN
culture NOUN
is AUX
really ADV
great ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Salary NOUN
, PUNCT
Commissions PROPN
, PUNCT
Life PROPN
Style PROPN
, PUNCT
Top PROPN
Management PROPN
Named Entity Recognition:
Commissions ORG
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
is AUX
an DET
International ADJ
company NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Amazing ADJ
colleagues NOUN
SPACE
Great ADJ
manager NOUN
SPACE
Great ADJ
work NOUN
Life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
everything PRON
clean ADJ
and CCONJ
new ADJ
technology NOUN
stuff NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Salary NOUN
, PUNCT
stability NOUN
, PUNCT
Size PROPN
, PUNCT
Carrier PROPN
opportunities NOUN
Named Entity Recognition:
Size ORG
Carrier PRODUCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
benefits NOUN
SPACE
Good PROPN
Work PROPN
Life PROPN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great PROPN
Team PROPN
, PUNCT
excellent ADJ
cantine ADJ
, PUNCT
interesting ADJ
tasts NOUN
Named Entity Recognition:
Great Team ORG
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
worked VERB
there ADV
for ADP
a DET
very ADV
short ADJ
period(2months NOUN
) PUNCT
but CCONJ
I PRON
had VERB
a DET
good ADJ
experience NOUN
with ADP
the DET
company NOUN
--------------------------------------------------
Tokenization and POS Tagging:
flexible PROPN
environment NOUN
, PUNCT
diversity NOUN
, PUNCT
complexity NOUN
of ADP
work NOUN
ok INTJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
The DET
bank NOUN
has VERB
a DET
really ADV
supportive ADJ
and CCONJ
strong ADJ
worker NOUN
’s PART
council NOUN
. PUNCT
Work NOUN
culture NOUN
varies VERB
across ADP
different ADJ
business NOUN
divisions NOUN
. PUNCT
You PRON
can AUX
develop VERB
your PRON
career NOUN
but CCONJ
need VERB
to PART
be AUX
willing ADJ
to PART
suck VERB
in ADP
the DET
overly ADV
political ADJ
environment NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
environment NOUN
and CCONJ
nice ADJ
colleagues NOUN
with ADP
sufficient ADJ
preparation NOUN
for ADP
Covid PROPN
19 NUM
situation NOUN
. PUNCT
some DET
flat ADJ
hierarchies NOUN
make VERB
the DET
reporting NOUN
line NOUN
easier ADV
. PUNCT
Named Entity Recognition:
19 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
sercure NOUN
workplace NOUN
, PUNCT
many ADJ
job NOUN
opportunities NOUN
within ADP
the DET
company NOUN
( PUNCT
Germany PROPN
) PUNCT
. PUNCT
some DET
options NOUN
abroad ADV
Named Entity Recognition:
Germany GPE
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
Good ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
SPACE
* PUNCT
Nice ADJ
colleagues NOUN
and CCONJ
pleasant ADJ
atmosphere NOUN
--------------------------------------------------
Processing 'Cons':
Tokenization and POS Tagging:
The DET
temporary ADJ
contract NOUN
and CCONJ
the DET
salary NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Globally ADV
lacking VERB
behind ADP
BB PROPN
US PROPN
banks NOUN
, PUNCT
but CCONJ
keeps VERB
up ADP
closely ADV
and CCONJ
ensures VERB
to PART
be AUX
lower ADJ
BB PROPN
Named Entity Recognition:
BB ORG
US GPE
BB ORG
--------------------------------------------------
Tokenization and POS Tagging:
Less ADV
challenging ADJ
for ADP
career NOUN
beginners NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
In ADP
Corona PROPN
times NOUN
difficult ADJ
to PART
network VERB
Named Entity Recognition:
Corona GPE
--------------------------------------------------
Tokenization and POS Tagging:
Monotone NOUN
work NOUN
and CCONJ
no DET
good ADJ
training NOUN
Named Entity Recognition:
Monotone PRODUCT
--------------------------------------------------
Tokenization and POS Tagging:
There PRON
is VERB
not PART
many ADJ
Career PROPN
Development PROPN
Opportunities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
i PRON
do AUX
nt PART
know VERB
anything PRON
about ADP
it PRON
thank VERB
you PRON
so ADV
much ADV
--------------------------------------------------
Tokenization and POS Tagging:
Heavy ADJ
workload NOUN
sometimes ADV
100h NUM
weeks NOUN
SPACE
Little ADJ
to ADP
no DET
understanding NOUN
for ADP
workloads NOUN
balance VERB
Named Entity Recognition:
100h weeks DATE
--------------------------------------------------
Tokenization and POS Tagging:
Internal ADJ
promotion NOUN
is AUX
difficult ADJ
Politics NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Responsibility NOUN
, PUNCT
a DET
lot NOUN
of ADP
admin NOUN
work NOUN
--------------------------------------------------
Tokenization and POS Tagging:
None NOUN
. PUNCT
There PRON
are VERB
no DET
cons NOUN
whatsoever ADV
--------------------------------------------------
Tokenization and POS Tagging:
none NOUN
so ADV
far ADV
, PUNCT
was AUX
great ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Slow ADJ
growth NOUN
and CCONJ
you PRON
might AUX
need VERB
to PART
push VERB
harder ADV
to PART
get VERB
things NOUN
moving VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
commute NOUN
time NOUN
, PUNCT
quite DET
a DET
bit NOUN
of ADP
red ADJ
tape NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
little ADJ
mentorship NOUN
and CCONJ
very ADV
vague ADJ
career NOUN
prospects NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Terrible ADJ
office NOUN
SPACE
Not PART
clear ADJ
policy NOUN
for ADP
promotion NOUN
--------------------------------------------------
Tokenization and POS Tagging:
can AUX
not PART
name VERB
any PRON
except SCONJ
usual ADJ
for ADP
large ADJ
banks NOUN
like ADP
bureaucracy NOUN
, PUNCT
poor ADJ
HR NOUN
processes NOUN
, PUNCT
no DET
full ADJ
remote ADJ
options NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
working VERB
hours NOUN
are AUX
typical ADJ
IB NOUN
hours NOUN
Named Entity Recognition:
The working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
Slow ADJ
moving NOUN
, PUNCT
you PRON
easily ADV
get AUX
burned VERB
out ADP
if SCONJ
you PRON
are AUX
devoted ADJ
to PART
innovate VERB
or CCONJ
establish VERB
sustainable ADJ
change NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
processes NOUN
can AUX
take VERB
too ADV
long ADV
including VERB
hiring VERB
and CCONJ
horizontal ADJ
mobility NOUN
within ADP
the DET
bank NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Low ADJ
pay NOUN
. PUNCT
No DET
raises VERB
. PUNCT
No DET
stocks NOUN
. PUNCT
Promotions NOUN
are AUX
a DET
lottery NOUN
. PUNCT
No DET
full ADJ
remote ADJ
, PUNCT
awful ADJ
office NOUN
spaces NOUN
. PUNCT
Poor ADJ
engineering NOUN
culture NOUN
. PUNCT
Dull NOUN
projects NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
You PRON
must AUX
enter VERB
at ADV
least ADV
5 NUM
words NOUN
for ADP
Cons PROPN
. PUNCT
Named Entity Recognition:
at least 5 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Challagning VERB
work NOUN
life NOUN
balance NOUN
etc NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
More ADJ
old ADJ
fashioned ADJ
, PUNCT
older ADJ
demographic NOUN
--------------------------------------------------
Tokenization and POS Tagging:
you PRON
might AUX
have VERB
to PART
travel VERB
, PUNCT
if SCONJ
you PRON
're AUX
not PART
into ADP
that PRON
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Slow ADJ
promotions NOUN
, PUNCT
no DET
clear ADJ
communication NOUN
on ADP
promotions NOUN
and CCONJ
bonuses NOUN
. PUNCT
Difficult ADJ
to PART
change VERB
roles NOUN
--------------------------------------------------
Tokenization and POS Tagging:
bank NOUN
going VERB
through ADP
a DET
cost NOUN
cutting NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
so ADV
cutting VERB
edge NOUN
tech NOUN
--------------------------------------------------
Tokenization and POS Tagging:
huge ADJ
product NOUN
knowledge NOUN
scattered VERB
in ADP
several ADJ
places NOUN
, PUNCT
SPACE
compliance NOUN
comes VERB
before ADP
innovation NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
strong ADJ
emphasis NOUN
on ADP
cost NOUN
management NOUN
for ADP
several ADJ
years NOUN
leading VERB
to ADP
lack NOUN
of ADP
opportunities NOUN
for ADP
external ADJ
self NOUN
- PUNCT
development NOUN
through ADP
conference NOUN
attendance NOUN
or CCONJ
trainings NOUN
, PUNCT
or CCONJ
indeed ADV
team NOUN
- PUNCT
building NOUN
through ADP
offsites NOUN
or CCONJ
in ADP
- PUNCT
person NOUN
meetings NOUN
. PUNCT
Named Entity Recognition:
several years DATE
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
alot NOUN
but CCONJ
beside ADP
the DET
Working PROPN
hours NOUN
Named Entity Recognition:
the Working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
Something PRON
’s VERB
everything PRON
was AUX
a DET
little ADJ
bit NOUN
stiff ADJ
--------------------------------------------------
Tokenization and POS Tagging:
No DET
benefits NOUN
other ADJ
than ADP
the DET
salary NOUN
--------------------------------------------------
Tokenization and POS Tagging:
nicht NOUN
agile ADJ
enough ADV
, PUNCT
often ADV
project NOUN
delays NOUN
Named Entity Recognition:
nicht ORG
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
to PART
say VERB
here ADV
it PRON
is AUX
exactly ADV
what PRON
you PRON
expect VERB
from ADP
a DET
big ADJ
company NOUN
hard ADJ
to PART
move VERB
upwards ADV
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
The DET
work NOUN
is AUX
super ADV
unorganized ADJ
. PUNCT
SPACE
- PUNCT
A DET
lot NOUN
of ADP
work NOUN
for ADP
the DET
money NOUN
, PUNCT
in ADP
Berlin PROPN
there PRON
is VERB
better ADJ
option NOUN
. PUNCT
SPACE
- PUNCT
It PRON
does AUX
n't PART
offers VERB
any DET
benefit NOUN
that PRON
other ADJ
tech NOUN
company NOUN
offers VERB
, PUNCT
for ADP
example NOUN
it PRON
does AUX
n't PART
offer VERB
the DET
1 NUM
month NOUN
remote ADJ
work NOUN
from ADP
anywhere ADV
in ADP
Europe PROPN
like ADP
any DET
other ADJ
companies NOUN
do VERB
. PUNCT
SPACE
- PUNCT
Toxic PROPN
office NOUN
environments NOUN
with ADP
huge ADJ
competitions NOUN
between ADP
colleagues NOUN
SPACE
- PUNCT
you PRON
feel VERB
alone ADV
SPACE
- PUNCT
Huge ADJ
amount NOUN
of ADP
overtime NOUN
( PUNCT
not PART
paid VERB
) PUNCT
Named Entity Recognition:
Berlin GPE
the 1 month DATE
Europe LOC
--------------------------------------------------
Tokenization and POS Tagging:
Slow ADJ
movement NOUN
to ADP
new ADJ
technologies NOUN
, PUNCT
but CCONJ
mostly ADV
stack NOUN
is AUX
up ADP
to ADP
date NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
to PART
say VERB
, PUNCT
really ADV
. PUNCT
Good ADJ
experience NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Random ADJ
department NOUN
rotations VERB
without ADP
matching VERB
your PRON
skills NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Hard ADJ
work NOUN
SPACE
No DET
time NOUN
SPACE
Stress NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Reputation NOUN
suffered VERB
due ADP
to ADP
leadership NOUN
--------------------------------------------------
Tokenization and POS Tagging:
very ADV
complex ADJ
as ADP
a DET
new ADJ
starter NOUN
to PART
navigate VERB
the DET
company NOUN
processes NOUN
, PUNCT
understand VERB
the DET
ways NOUN
of ADP
working VERB
and CCONJ
understand VERB
the DET
tasks NOUN
expected VERB
of ADP
you PRON
. PUNCT
SPACE
I PRON
would AUX
only ADV
attempt VERB
to PART
work VERB
here ADV
if SCONJ
you PRON
have VERB
strong ADJ
resilience NOUN
( PUNCT
grit NOUN
) PUNCT
to ADP
a DET
huge ADJ
amounts NOUN
of ADP
uncertainty NOUN
, PUNCT
daily ADJ
surprises NOUN
and CCONJ
last ADJ
minute NOUN
requests NOUN
. PUNCT
It PRON
's AUX
a DET
very ADV
unstructured ADJ
place NOUN
to PART
work VERB
. PUNCT
But CCONJ
I PRON
see VERB
so ADV
much ADJ
potential NOUN
for ADP
process NOUN
optimisation NOUN
and CCONJ
that PRON
's AUX
what PRON
motivates VERB
me PRON
to PART
stay VERB
Named Entity Recognition:
daily DATE
last minute TIME
--------------------------------------------------
Tokenization and POS Tagging:
No DET
possibility NOUN
to PART
work VERB
from ADP
abroad ADV
--------------------------------------------------
Tokenization and POS Tagging:
There PRON
may AUX
be AUX
disadvantages NOUN
only ADV
depending VERB
on ADP
the DET
individual ADJ
project NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Slow ADJ
move NOUN
to ADP
upper ADJ
levels NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
stressful ADJ
, PUNCT
because SCONJ
of ADP
big ADJ
reorganisation NOUN
changes NOUN
we PRON
lost VERB
some DET
our PRON
team NOUN
members NOUN
and CCONJ
need VERB
to PART
manage VERB
now ADV
without ADP
them PRON
, PUNCT
utill ADJ
we PRON
hire VERB
more ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
none NOUN
of ADP
bad ADJ
experience NOUN
at ADP
this DET
company NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Unfortunately ADV
the DET
salty NOUN
so ADV
not PART
in ADP
the DET
high ADJ
range NOUN
as ADP
other ADJ
companies NOUN
in ADP
the DET
same ADJ
field NOUN
. PUNCT
Depending VERB
on ADP
the DET
department NOUN
and CCONJ
team NOUN
, PUNCT
you PRON
might AUX
get VERB
a DET
stable ADJ
salary NOUN
increase NOUN
and CCONJ
jump VERB
into ADP
the DET
career NOUN
but CCONJ
in ADP
Berlin PROPN
unfortunately ADV
can AUX
be AUX
quite ADV
slow ADJ
to PART
climb VERB
up ADP
. PUNCT
If SCONJ
you PRON
move VERB
I PRON
internally ADV
to ADP
a DET
higher ADJ
role NOUN
you PRON
might AUX
still ADV
have VERB
to PART
wait VERB
1year NUM
and CCONJ
half NOUN
before ADP
getting VERB
the DET
title NOUN
you PRON
applied VERB
to ADP
. PUNCT
Named Entity Recognition:
Berlin GPE
1year DATE
half CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Lots NOUN
of ADP
restrictions NOUN
typical ADJ
for ADP
bank NOUN
employers NOUN
--------------------------------------------------
Tokenization and POS Tagging:
nothing PRON
really ADV
good ADJ
atmosphere NOUN
and CCONJ
spirit NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
atmosphere NOUN
was AUX
not PART
very ADV
nice ADJ
. PUNCT
Bosses NOUN
were AUX
not PART
always ADV
respectful ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
slow ADJ
proccess NOUN
, PUNCT
takes VERB
a DET
lot NOUN
of ADP
time NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Slow ADJ
career NOUN
progression NOUN
. PUNCT
Hard ADJ
to PART
get AUX
promoted VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Beurocrasy PROPN
, PUNCT
business NOUN
is AUX
in ADP
own ADJ
budget NOUN
and CCONJ
separated VERB
from ADP
IT PRON
, PUNCT
but CCONJ
depends VERB
from ADP
IT PRON
with ADP
all DET
further ADJ
consiquenses NOUN
, PUNCT
a DET
lot NOUN
of ADP
internal ADJ
and CCONJ
external ADJ
audit NOUN
and CCONJ
compliance NOUN
streams NOUN
, PUNCT
that SCONJ
development NOUN
groups NOUN
has VERB
to PART
deal VERB
with ADP
, PUNCT
very ADV
inefficient ADJ
, PUNCT
obsolete ADJ
and CCONJ
suboptimal ADJ
environment NOUN
management NOUN
tools NOUN
, PUNCT
sometimes ADV
contradictory ADJ
, PUNCT
even ADV
idiotic ADJ
policies NOUN
about ADP
environment NOUN
management NOUN
Named Entity Recognition:
Beurocrasy WORK_OF_ART
--------------------------------------------------
Tokenization and POS Tagging:
You PRON
will AUX
never ADV
earn VERB
good ADJ
money NOUN
with ADP
DB PROPN
. PUNCT
Named Entity Recognition:
DB ORG
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
a DET
bit NOUN
hierarchical ADJ
and CCONJ
conservative ADJ
- PUNCT
internship NOUN
was AUX
a PRON
but CCONJ
unstructured ADJ
--------------------------------------------------
Tokenization and POS Tagging:
High ADJ
pressure NOUN
Poor ADJ
work NOUN
life NOUN
balance NOUN
No DET
education NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Is AUX
chaotic ADJ
and CCONJ
processes NOUN
take VERB
forever ADV
--------------------------------------------------
Tokenization and POS Tagging:
Employer NOUN
does AUX
n't PART
care VERB
about ADP
providing VERB
kicker NOUN
table NOUN
to ADP
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Being AUX
in ADP
the DET
Berlin PROPN
office NOUN
you PRON
are AUX
away ADV
from ADP
the DET
HQ PROPN
hence ADV
lower ADJ
visibility NOUN
. PUNCT
Named Entity Recognition:
Berlin GPE
HQ GPE
--------------------------------------------------
Tokenization and POS Tagging:
Big ADJ
company NOUN
with ADP
bureaucracy NOUN
, PUNCT
Finance PROPN
sector NOUN
mean VERB
security NOUN
and CCONJ
restrictions NOUN
, PUNCT
usually ADV
if SCONJ
you PRON
chose VERB
wrong ADJ
project NOUN
, PUNCT
you PRON
will AUX
have VERB
next ADJ
attempt NOUN
in ADP
next ADJ
year NOUN
. PUNCT
Named Entity Recognition:
next year DATE
--------------------------------------------------
Tokenization and POS Tagging:
Opportunity NOUN
to PART
grow VERB
Diversity PROPN
and CCONJ
inclusion NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Being AUX
a DET
big ADJ
company NOUN
, PUNCT
the DET
processes NOUN
are AUX
too ADV
bureaucratic ADJ
--------------------------------------------------
Tokenization and POS Tagging:
The DET
internship NOUN
program NOUN
was AUX
not PART
very ADV
social ADJ
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
have VERB
nto PROPN
found VERB
any DET
cons NOUN
yet ADV
--------------------------------------------------
Tokenization and POS Tagging:
No DET
development NOUN
, PUNCT
no DET
training NOUN
possibilities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
In ADP
- PUNCT
house NOUN
food NOUN
options NOUN
were AUX
too ADV
expensive ADJ
for ADP
a DET
comparatively ADV
low ADJ
quality NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Legacy PROPN
brings VERB
past ADV
into ADP
the DET
present NOUN
constantly ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
politics NOUN
for ADP
career NOUN
growth NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Career NOUN
only ADV
based VERB
on ADP
networking VERB
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
bureaucracy NOUN
- PUNCT
slow ADJ
adapting NOUN
of ADP
new ADJ
technologies NOUN
--------------------------------------------------
Tokenization and POS Tagging:
No DET
interesting ADJ
work NOUN
given VERB
to ADP
interns NOUN
--------------------------------------------------
Tokenization and POS Tagging:
i PRON
hated VERB
the DET
stress NOUN
at ADP
work NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Security NOUN
restrictions NOUN
and CCONJ
paper NOUN
work NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Complex PROPN
IT NOUN
systems NOUN
might AUX
slow VERB
down ADP
processes NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Tons NOUN
of ADP
bureaucracy NOUN
, PUNCT
legacy NOUN
projects NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Deutsche PROPN
Bank PROPN
can AUX
be AUX
quite ADV
bureaucratic ADJ
Named Entity Recognition:
Deutsche Bank ORG
--------------------------------------------------
Tokenization and POS Tagging:
not PART
many ADJ
cons NOUN
for ADP
working VERB
at ADP
d PROPN
bank NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Politically ADV
very ADV
correct ADJ
and CCONJ
you PRON
see VERB
internal ADJ
communications NOUN
tailored VERB
to ADP
it PRON
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Lots NOUN
of ADP
process NOUN
and CCONJ
slow VERB
--------------------------------------------------
Tokenization and POS Tagging:
Workload NOUN
and CCONJ
tasks NOUN
within ADP
the DET
rotations NOUN
solely ADV
depend VERB
on ADP
the DET
teams NOUN
you PRON
rotate VERB
too ADV
. PUNCT
There PRON
should AUX
be AUX
a DET
general ADJ
procedure NOUN
--------------------------------------------------
Tokenization and POS Tagging:
At ADP
least ADJ
in ADP
departments NOUN
that PRON
are AUX
n't PART
about ADP
generating VERB
revenue NOUN
, PUNCT
there PRON
's VERB
not PART
much ADV
advanced ADJ
technology NOUN
to PART
work VERB
with ADP
and CCONJ
there PRON
's VERB
a DET
lot NOUN
of ADP
red ADJ
tape NOUN
in ADP
trying VERB
to PART
improve VERB
processes NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
could AUX
get VERB
intense ADJ
Not PART
easy ADJ
to PART
get VERB
Salary NOUN
increase NOUN
Compensation PROPN
is AUX
not PART
competitive ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
to PART
bad ADJ
at ADV
all ADV
--------------------------------------------------
Tokenization and POS Tagging:
Ofcourse INTJ
nothing PRON
much ADV
same ADJ
as ADP
other ADJ
big ADJ
companies NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Dress NOUN
code NOUN
can AUX
be AUX
a DET
problem NOUN
--------------------------------------------------
Tokenization and POS Tagging:
not PART
as ADV
friendly ADJ
for ADP
non ADJ
- ADJ
european ADJ
Named Entity Recognition:
non-european NORP
--------------------------------------------------
Tokenization and POS Tagging:
Having VERB
lots NOUN
of ADP
regulations NOUN
that PRON
are AUX
required VERB
to PART
be AUX
met VERB
. PUNCT
Not PART
enough ADV
/ SYM
very ADV
busy ADJ
people NOUN
in ADP
infra ADJ
teams NOUN
to PART
provide VERB
quick ADJ
support NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Restrictions NOUN
, PUNCT
salary NOUN
level NOUN
, PUNCT
impossible ADJ
to PART
upgrade VERB
your PRON
rank NOUN
--------------------------------------------------
Tokenization and POS Tagging:
A DET
classic ADJ
, PUNCT
burocratic ADJ
, PUNCT
banking NOUN
enironment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Bad ADJ
work NOUN
life NOUN
balance NOUN
Legacy PROPN
code NOUN
Named Entity Recognition:
Legacy code PRODUCT
--------------------------------------------------
Tokenization and POS Tagging:
nothing PRON
at ADV
all ADV
. PUNCT
just ADV
the DET
starting NOUN
salary NOUN
could AUX
be AUX
higher ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
tech NOUN
related VERB
topics NOUN
feel VERB
a DET
bit NOUN
outdated ADJ
. PUNCT
Not PART
the DET
most ADV
tech ADJ
savy ADJ
company NOUN
, PUNCT
nor CCONJ
the DET
most ADV
engineering NOUN
first ADV
minded ADJ
. PUNCT
Onboarding NOUN
not PART
great ADJ
. PUNCT
Hardware NOUN
not PART
great ADJ
, PUNCT
special ADJ
request NOUN
if SCONJ
you PRON
need VERB
a DET
Macbook PROPN
as ADP
a DET
developer NOUN
, PUNCT
, PUNCT
Named Entity Recognition:
first ORDINAL
Macbook ORG
--------------------------------------------------
Tokenization and POS Tagging:
Still ADV
Very ADV
bureaucratic ADJ
and CCONJ
old ADJ
school NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Long ADJ
, PUNCT
intense ADJ
and CCONJ
stressful ADJ
hours NOUN
Named Entity Recognition:
hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
From ADP
my PRON
perspective NOUN
not PART
so ADV
many ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Promotion NOUN
is AUX
promised VERB
but CCONJ
does AUX
not PART
happen VERB
to ADP
many ADJ
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
male ADJ
colleagues NOUN
exhibit VERB
toxic ADJ
behavior NOUN
--------------------------------------------------
Tokenization and POS Tagging:
At ADP
closed ADJ
days NOUN
there PRON
was VERB
not PART
much ADJ
to PART
do VERB
but CCONJ
either DET
way NOUN
was AUX
I PRON
able ADJ
to PART
gain VERB
more ADV
theoretical ADJ
knowledge NOUN
about ADP
banking NOUN
while SCONJ
waiting VERB
for ADP
new ADJ
tasks NOUN
. PUNCT
Named Entity Recognition:
days DATE
--------------------------------------------------
Tokenization and POS Tagging:
Long ADJ
hours NOUN
, PUNCT
no DET
cons NOUN
at ADV
all ADV
--------------------------------------------------
Tokenization and POS Tagging:
There PRON
is VERB
not PART
vision NOUN
to PART
improve VERB
--------------------------------------------------
Tokenization and POS Tagging:
As ADP
at ADP
big ADJ
companies NOUN
quite ADV
often ADV
you PRON
can AUX
face VERB
with ADP
bureaucracy NOUN
--------------------------------------------------
Tokenization and POS Tagging:
No DET
career NOUN
development NOUN
or CCONJ
support NOUN
--------------------------------------------------
Tokenization and POS Tagging:
No DET
certain ADJ
way NOUN
to PART
ensure VERB
your PRON
career NOUN
--------------------------------------------------
Tokenization and POS Tagging:
no DET
cons NOUN
, PUNCT
nothing PRON
to PART
say VERB
here ADV
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
is AUX
n't PART
always ADV
the DET
most ADV
exciting ADJ
and CCONJ
there PRON
's VERB
a DET
lot NOUN
of ADP
tardiness NOUN
at ADP
each DET
step NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Corporate ADJ
structure NOUN
, PUNCT
career NOUN
path NOUN
delays NOUN
when SCONJ
starting VERB
--------------------------------------------------
Tokenization and POS Tagging:
-company ADJ
culture NOUN
-not VERB
many ADJ
activities NOUN
for ADP
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
quite ADV
conservative ADJ
, PUNCT
lack NOUN
of ADP
clear ADJ
internal ADJ
communication NOUN
--------------------------------------------------
Tokenization and POS Tagging:
There PRON
is VERB
not PART
much ADJ
communication NOUN
--------------------------------------------------
Tokenization and POS Tagging:
sometimes ADV
unnecessary ADJ
work NOUN
, PUNCT
back ADV
and CCONJ
forth ADV
with ADP
other ADJ
teams NOUN
--------------------------------------------------
Tokenization and POS Tagging:
A DET
lot NOUN
of ADP
legacy NOUN
. PUNCT
Competition NOUN
between ADP
teams NOUN
for ADP
resources NOUN
so ADV
two NUM
teams NOUN
can AUX
do VERB
the DET
same ADJ
to PART
achieve VERB
promotion NOUN
goals NOUN
. PUNCT
There PRON
are VERB
no DET
cooperation NOUN
between ADP
teams NOUN
to PART
reach VERB
organisation NOUN
goals NOUN
. PUNCT
Named Entity Recognition:
two CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Below ADP
the DET
average ADJ
salary NOUN
level NOUN
for ADP
high ADJ
skilled ADJ
employees NOUN
. PUNCT
Promotion NOUN
requirements NOUN
for ADP
technically ADV
inclined ADJ
employees NOUN
are AUX
vague ADJ
and CCONJ
the DET
final ADJ
decision NOUN
usually ADV
depends VERB
on ADP
personal ADJ
relationship NOUN
with ADP
the DET
manager NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Bureaucracy NOUN
and CCONJ
extra ADJ
hours NOUN
are AUX
not PART
being AUX
paid VERB
--------------------------------------------------
Tokenization and POS Tagging:
Workload NOUN
, PUNCT
tiring ADJ
, PUNCT
client NOUN
requirements NOUN
adhoc ADV
Named Entity Recognition:
adhoc TIME
--------------------------------------------------
Tokenization and POS Tagging:
Low ADJ
growth NOUN
potential NOUN
, PUNCT
demotivating VERB
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
No DET
Training PROPN
Bonus PROPN
, PUNCT
Lots NOUN
of ADP
Budget PROPN
cuts NOUN
, PUNCT
No DET
travel NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Bureaucracy NOUN
and CCONJ
Management PROPN
, PUNCT
work NOUN
life NOUN
imbalance NOUN
, PUNCT
unrealistic ADJ
targets NOUN
Named Entity Recognition:
Bureaucracy and Management ORG
--------------------------------------------------
Tokenization and POS Tagging:
long ADJ
commute NOUN
, PUNCT
no DET
parking NOUN
places NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Work VERB
from ADP
home NOUN
only ADV
2 NUM
days NOUN
a DET
week NOUN
! PUNCT
Mostly ADV
Russian ADJ
staff NOUN
speaks VERB
in ADP
Russian PROPN
in ADP
office NOUN
. PUNCT
Named Entity Recognition:
only 2 days DATE
Russian NORP
Russian NORP
--------------------------------------------------
Tokenization and POS Tagging:
Salary NOUN
, PUNCT
bureaucracy NOUN
, PUNCT
promotion NOUN
process NOUN
, PUNCT
legacy PROPN
code NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Burocracy NOUN
, PUNCT
difficult ADJ
and CCONJ
slow ADJ
promotion NOUN
--------------------------------------------------
Tokenization and POS Tagging:
there PRON
is VERB
a DET
ceiling NOUN
when SCONJ
it PRON
comes VERB
to ADP
corporate ADJ
title NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
Corporate ADJ
environment NOUN
. PUNCT
old ADJ
fashioned VERB
--------------------------------------------------
Tokenization and POS Tagging:
Too ADV
many ADJ
tasks NOUN
that PRON
we PRON
were AUX
n't PART
ready ADJ
to PART
handle VERB
as SCONJ
we PRON
were AUX
only ADV
two NUM
interns NOUN
. PUNCT
Named Entity Recognition:
only two CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Bureaucracy NOUN
, PUNCT
politics NOUN
, PUNCT
slow ADJ
hiring NOUN
processes NOUN
, PUNCT
poor ADJ
IT PROPN
infrastructure NOUN
, PUNCT
slow ADJ
promotion NOUN
process NOUN
, PUNCT
cost NOUN
savings NOUN
, PUNCT
non NOUN
- NOUN
appreciation NOUN
of ADP
current ADJ
staff NOUN
by ADP
some DET
managers NOUN
( PUNCT
same ADJ
salary NOUN
and CCONJ
conditions NOUN
as ADP
for ADP
external ADJ
new ADJ
joiners NOUN
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Compensation NOUN
development NOUN
, PUNCT
team NOUN
& CCONJ
training NOUN
budget NOUN
--------------------------------------------------
Tokenization and POS Tagging:
GOOD ADJ
asmotsphere NOUN
, PUNCT
pay VERB
good ADJ
, PUNCT
nice ADJ
co NOUN
- NOUN
workers NOUN
Named Entity Recognition:
GOOD asmotsphere PERSON
--------------------------------------------------
Tokenization and POS Tagging:
Bad ADJ
work NOUN
life NOUN
balance NOUN
Bad ADJ
company NOUN
governance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
hierarchical ADJ
and CCONJ
silo ADJ
organisation NOUN
* PUNCT
no DET
investment NOUN
into ADP
own ADJ
human ADJ
capital NOUN
( PUNCT
limited ADJ
training NOUN
and CCONJ
continuous ADJ
development NOUN
, PUNCT
nearly ADV
every PRON
professional ADJ
fades VERB
away ADV
in ADP
a DET
bureaucratic ADJ
processes NOUN
, PUNCT
specialist NOUN
and CCONJ
IT NOUN
experts NOUN
loosing VERB
hard ADJ
skills NOUN
and CCONJ
becoming VERB
project NOUN
managers NOUN
of ADP
external ADJ
employees NOUN
) PUNCT
* PUNCT
lots NOUN
of ADP
about ADP
company NOUN
culture NOUN
and CCONJ
technology NOUN
is AUX
only ADV
a DET
marketing NOUN
, PUNCT
in ADP
reality NOUN
technology NOUN
and CCONJ
human NOUN
processes NOUN
are AUX
like ADP
10 NUM
- SYM
20 NUM
years NOUN
ago ADV
, PUNCT
fake ADJ
speak NOUN
- PUNCT
up NOUN
, PUNCT
agile ADJ
and CCONJ
leadership NOUN
culture NOUN
. PUNCT
* PUNCT
low ADJ
transparency NOUN
of ADP
promotion NOUN
rules NOUN
, PUNCT
nepotism NOUN
of ADP
internal ADJ
networks NOUN
* PUNCT
HR NOUN
is AUX
consistently ADV
working VERB
against ADP
own ADJ
employees NOUN
( PUNCT
ca AUX
n't PART
do AUX
, PUNCT
ca AUX
n't PART
support VERB
, PUNCT
squeeze NOUN
salaries NOUN
) PUNCT
* PUNCT
remuneration NOUN
is AUX
systematically ADV
8%-15 NUM
% NOUN
lower ADJ
then ADV
with ADP
competitors NOUN
, PUNCT
longer ADV
a DET
person NOUN
stay VERB
, PUNCT
worse ADJ
it PRON
is AUX
. PUNCT
* PUNCT
Poor ADJ
Work NOUN
from ADP
Home NOUN
( PUNCT
cost VERB
heavily ADV
on ADP
employees NOUN
) PUNCT
, PUNCT
limited VERB
only ADV
on ADP
Germany PROPN
, PUNCT
poor ADJ
technology NOUN
to PART
support VERB
effectiveness NOUN
. PUNCT
Named Entity Recognition:
10-20 years ago DATE
8%-15% PERCENT
Germany GPE
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
underpayment NOUN
* PUNCT
legacy NOUN
tech NOUN
stack NOUN
if SCONJ
you PRON
are AUX
not PART
so ADV
lucky ADJ
--------------------------------------------------
Tokenization and POS Tagging:
No DET
cons NOUN
to PART
mention VERB
here ADV
--------------------------------------------------
Tokenization and POS Tagging:
no DET
fixed VERB
income NOUN
, PUNCT
only ADV
provisions NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
the DET
systems NOUN
are AUX
quite ADV
old ADJ
- PUNCT
everything PRON
takes VERB
time NOUN
-too ADV
many ADJ
approvals NOUN
to PART
make VERB
change NOUN
- PUNCT
repetitive ADJ
tasks NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Large ADJ
firm NOUN
, PUNCT
politics NOUN
, PUNCT
good ADJ
old ADJ
boy NOUN
style NOUN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
think VERB
this PRON
is AUX
Lots NOUN
of ADP
bureaucracy NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
slow ADJ
process NOUN
overall ADV
. PUNCT
Sometimes ADV
it PRON
becomes VERB
boring ADJ
when SCONJ
it PRON
comes VERB
to ADP
process NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Not PART
many ADJ
events NOUN
between ADP
different ADJ
departments NOUN
to PART
get VERB
to PART
know VERB
each DET
other ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Too ADV
many ADJ
people NOUN
speak VERB
german NOUN
Named Entity Recognition:
german NORP
--------------------------------------------------
Tokenization and POS Tagging:
poor ADJ
management NOUN
, PUNCT
everything PRON
takes VERB
forever ADV
--------------------------------------------------
Tokenization and POS Tagging:
everything PRON
is AUX
bureaucratic ADJ
and CCONJ
slow ADJ
. PUNCT
nothing PRON
can AUX
be AUX
solved VERB
quickly ADV
. PUNCT
MY NOUN
department NOUN
had VERB
no DET
career NOUN
plan NOUN
or CCONJ
progression NOUN
with ADP
workers NOUN
doing VERB
the DET
same ADJ
work NOUN
with ADP
the DET
same ADJ
pay NOUN
/ SYM
title NOUN
for ADP
6 NUM
years NOUN
. PUNCT
No DET
one NOUN
gives VERB
you PRON
a DET
straight ADJ
answer NOUN
about ADP
anything PRON
. PUNCT
Hard ADV
to PART
get VERB
things NOUN
done VERB
. PUNCT
Named Entity Recognition:
6 years DATE
--------------------------------------------------
Tokenization and POS Tagging:
Pays NOUN
less ADJ
than ADP
competitors NOUN
in ADP
the DET
market NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Bureaucracy NOUN
, PUNCT
projects NOUN
can AUX
have VERB
long ADJ
and CCONJ
slow ADJ
implementation NOUN
timelines NOUN
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
have VERB
a DET
lot NOUN
of ADP
bureaucracy NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Still ADV
some DET
way NOUN
to PART
go VERB
regarding VERB
infrastructure NOUN
but CCONJ
heading VERB
in ADP
the DET
right ADJ
direction NOUN
--------------------------------------------------
Tokenization and POS Tagging:
no DET
remote ADJ
working NOUN
, PUNCT
bad ADJ
work NOUN
- PUNCT
life NOUN
- PUNCT
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Bit VERB
slow ADV
on ADP
decisions NOUN
making VERB
and CCONJ
changes NOUN
, PUNCT
but CCONJ
it PRON
is AUX
a DET
huge ADJ
organization NOUN
and CCONJ
makes VERB
sense NOUN
sometimes ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Tech NOUN
is AUX
bad ADJ
and CCONJ
slow VERB
Named Entity Recognition:
Tech ORG
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Teilweise NOUN
schwierige VERB
Prozesse PROPN
SPACE
- PUNCT
Langjährige PROPN
Mitarbeiter PROPN
beharren PROPN
auf PROPN
alte PROPN
Vorgehensweisen PROPN
Named Entity Recognition:
schwierige Prozesse
- PERSON
--------------------------------------------------
Tokenization and POS Tagging:
work VERB
slow ADJ
and CCONJ
boring ADJ
SPACE
progression NOUN
and CCONJ
salary NOUN
increment NOUN
not PART
so ADV
promising VERB
--------------------------------------------------
Tokenization and POS Tagging:
The DET
Working PROPN
subjects NOUN
were AUX
rather ADV
boring ADJ
, PUNCT
no DET
change NOUN
in ADP
topics NOUN
SPACE
Difficult ADJ
management NOUN
, PUNCT
SPACE
The DET
place NOUN
to PART
work NOUN
was AUX
a DET
bit NOUN
far ADV
away ADV
. PUNCT
Furthermore ADV
, PUNCT
" PUNCT
flexible ADJ
" PUNCT
seating NOUN
was AUX
announced VERB
. PUNCT
However ADV
, PUNCT
all DET
other ADJ
team NOUN
mebers NOUN
had VERB
their PRON
" PUNCT
fixed VERB
" PUNCT
seats NOUN
Named Entity Recognition:
Working ORG
--------------------------------------------------
Tokenization and POS Tagging:
Internationally ADV
there PRON
are VERB
stronger ADJ
brands NOUN
; PUNCT
if SCONJ
you PRON
want VERB
to PART
pursue VERB
international ADJ
career NOUN
( PUNCT
UK PROPN
, PUNCT
US PROPN
) PUNCT
, PUNCT
DB PROPN
is AUX
potentially ADV
not PART
the DET
type NOUN
of ADP
brand NOUN
it PRON
used VERB
to PART
be AUX
few ADJ
years NOUN
ago ADV
Named Entity Recognition:
UK GPE
US GPE
DB ORG
few years ago DATE
--------------------------------------------------
Tokenization and POS Tagging:
Sometimes ADV
the DET
possibilities NOUN
of ADP
promotion NOUN
are AUX
low ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
slow ADJ
processes NOUN
, PUNCT
bad ADJ
career NOUN
prospects NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Pay NOUN
could AUX
be AUX
better ADJ
and CCONJ
flexible ADJ
working NOUN
could AUX
be AUX
more ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
No DET
cons NOUN
so ADV
far ADV
in ADP
the DET
org NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
sometimes ADV
they PRON
do AUX
not PART
have VERB
official ADJ
internship NOUN
programs NOUN
that PRON
leads VERB
to ADP
you PRON
sometimes ADV
doing VERB
less ADJ
than ADP
other ADJ
interns NOUN
--------------------------------------------------
Tokenization and POS Tagging:
overload NOUN
with ADP
work NOUN
, PUNCT
poor ADJ
life NOUN
- PUNCT
work NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Working ADJ
hours NOUN
, PUNCT
salary NOUN
dumping NOUN
, PUNCT
internal ADJ
career NOUN
progress NOUN
ist NOUN
complex NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Hybrid NOUN
not PART
remote ADJ
remote ADJ
remote NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Cafeteria PROPN
is AUX
too ADV
expensive ADJ
and CCONJ
no DET
lunch NOUN
support NOUN
as ADP
intern NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Bad ADJ
management NOUN
is AUX
driving VERB
talent NOUN
away ADV
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
think VERB
that SCONJ
sometimes ADV
have VERB
a DET
traffic NOUN
jam NOUN
outside ADP
--------------------------------------------------
Tokenization and POS Tagging:
Bureaucratic ADJ
, PUNCT
slow ADJ
, PUNCT
poor ADJ
data NOUN
availability NOUN
and CCONJ
governance NOUN
, PUNCT
few ADJ
to ADP
no DET
opportunities NOUN
to PART
meet VERB
all DET
team NOUN
members NOUN
in ADP
person NOUN
or CCONJ
even ADV
by ADP
video NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Too ADV
much ADJ
work NOUN
SPACE
Not PART
remote ADJ
SPACE
Can AUX
be AUX
a DET
bit NOUN
better ADJ
in ADP
terms NOUN
of ADP
pay NOUN
SPACE
Can AUX
have VERB
better ADJ
perks NOUN
SPACE
Limited PROPN
paid VERB
leave NOUN
Named Entity Recognition:
Limited ORG
--------------------------------------------------
Tokenization and POS Tagging:
wages NOUN
, PUNCT
opportunities NOUN
, PUNCT
wrong ADJ
location NOUN
, PUNCT
and CCONJ
that PRON
s VERB
it PRON
--------------------------------------------------
Tokenization and POS Tagging:
Typical ADJ
multinational ADJ
environment NOUN
SPACE
Compensation PROPN
could AUX
be AUX
better ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Compensation NOUN
is AUX
low ADJ
compared VERB
to ADP
other ADJ
employer NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Company NOUN
vision NOUN
changes VERB
too ADV
frequently ADV
SPACE
Management PROPN
wants VERB
to PART
invest VERB
in ADP
technology NOUN
but CCONJ
can AUX
never ADV
deliver VERB
--------------------------------------------------
Tokenization and POS Tagging:
No DET
real ADJ
cons NOUN
in ADP
the DET
working VERB
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Its PRON
a DET
bank NOUN
not PART
a DET
FANG PROPN
like ADP
company NOUN
. PUNCT
Do AUX
nt PART
expect VERB
fancy ADJ
staff NOUN
here ADV
:) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
just ADV
started VERB
there ADV
. PUNCT
Would AUX
add VERB
them PRON
when SCONJ
I PRON
know VERB
--------------------------------------------------
Tokenization and POS Tagging:
Teilweise INTJ
unfreundliche PROPN
Mitarbeiter PROPN
, PUNCT
kommt PROPN
öfter PROPN
vor PROPN
Named Entity Recognition:
Mitarbeiter GPE
kommt öfter vor PERSON
--------------------------------------------------
Tokenization and POS Tagging:
lack NOUN
of ADP
support NOUN
for ADP
working VERB
from ADP
home NOUN
--------------------------------------------------
Tokenization and POS Tagging:
long ADV
working VERB
hours NOUN
and CCONJ
high ADJ
pressure NOUN
Named Entity Recognition:
long working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
The DET
organization NOUN
is AUX
somewhat ADV
hierarchical ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Rigid ADJ
structure NOUN
SPACE
Focus PROPN
on ADP
corporate ADJ
politics NOUN
not PART
capabilities NOUN
or CCONJ
deliverables NOUN
SPACE
In ADP
order NOUN
to PART
further VERB
your PRON
career NOUN
you PRON
must AUX
focus VERB
on ADP
knowing VERB
people NOUN
, PUNCT
letting VERB
problems NOUN
compound VERB
and CCONJ
increase NOUN
, PUNCT
produce VERB
powerpoints NOUN
and CCONJ
hold VERB
presentations NOUN
on ADP
how SCONJ
you PRON
will AUX
be AUX
the DET
savior NOUN
. PUNCT
Named Entity Recognition:
Focus ORG
--------------------------------------------------
Tokenization and POS Tagging:
Big ADJ
organisation NOUN
and CCONJ
slow ADJ
decisions NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
slow ADJ
to PART
adopt VERB
to ADP
new ADJ
technologies NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
depends VERB
very ADV
much ADV
on ADP
department NOUN
, PUNCT
but CCONJ
sinking VERB
and CCONJ
negative ADJ
atmosphere NOUN
, PUNCT
no DET
one NOUN
cares VERB
about ADP
your PRON
feel NOUN
, PUNCT
forget VERB
about ADP
support NOUN
from ADP
senior ADJ
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Typical ADJ
German ADJ
bureaucracy NOUN
. PUNCT
SPACE
Way NOUN
behind ADP
other ADJ
companies NOUN
/ SYM
banks NOUN
in ADP
implementing VERB
new ADJ
stuff NOUN
Named Entity Recognition:
German NORP
--------------------------------------------------
Tokenization and POS Tagging:
Lots NOUN
of ADP
office NOUN
politics NOUN
and CCONJ
a DET
horrible ADJ
promotion NOUN
process NOUN
--------------------------------------------------
Tokenization and POS Tagging:
at ADP
times NOUN
brutal ADJ
hours NOUN
, PUNCT
processes NOUN
from ADP
a DET
time NOUN
when SCONJ
the DET
company NOUN
was AUX
2x NOUN
the DET
size NOUN
, PUNCT
corporate ADJ
red ADJ
tape NOUN
at ADP
its PRON
worst ADJ
Named Entity Recognition:
hours TIME
2x CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Difficult ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
in ADP
Senior PROPN
Positions NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
hierarchical ADJ
, PUNCT
little ADJ
room NOUN
for ADP
own ADJ
initiatives NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Hard ADJ
work NOUN
spare ADJ
time NOUN
City PROPN
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
takes VERB
time NOUN
to PART
be AUX
senior ADJ
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
could AUX
say VERB
nothing PRON
here ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
has VERB
too ADV
many ADJ
Litigations PROPN
against ADP
it PRON
reflecting VERB
bonus NOUN
. PUNCT
Named Entity Recognition:
Litigations ORG
--------------------------------------------------
Tokenization and POS Tagging:
Things NOUN
can AUX
move VERB
a DET
little ADJ
slowly ADV
. PUNCT
Not PART
the DET
place NOUN
to PART
be AUX
if SCONJ
you PRON
are AUX
looking VERB
for ADP
state NOUN
of ADP
the DET
art NOUN
tech NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
old ADJ
people NOUN
, PUNCT
fat ADJ
people NOUN
and CCONJ
dumb ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Dynamics NOUN
, PUNCT
complex ADJ
structure NOUN
, PUNCT
lack NOUN
of ADP
elasticity NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Slow ADJ
pace NOUN
SPACE
Many ADJ
processes NOUN
SPACE
Old PROPN
tech NOUN
stack NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
usual ADJ
cons NOUN
that PRON
come VERB
with ADP
working VERB
for ADP
a DET
big ADJ
corporate ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Had VERB
to PART
work VERB
for ADP
a DET
night NOUN
shift NOUN
unfortunately ADV
Named Entity Recognition:
night TIME
--------------------------------------------------
Tokenization and POS Tagging:
Poltitically PROPN
games NOUN
playing VERB
people NOUN
stay VERB
and CCONJ
good ADJ
people NOUN
leave VERB
SPACE
no DET
difference NOUN
in ADP
salary NOUN
for ADP
10 NUM
year NOUN
and CCONJ
2 NUM
years NOUN
experienced VERB
Named Entity Recognition:
10 year DATE
2 years DATE
--------------------------------------------------
Tokenization and POS Tagging:
Really ADV
bad ADJ
IT NOUN
infrastructure NOUN
. PUNCT
Loyalty NOUN
is AUX
valued VERB
more ADJ
than ADP
actual ADJ
skills NOUN
so SCONJ
there PRON
’s VERB
a DET
lot NOUN
of ADP
unskilled ADJ
people NOUN
in ADP
higher ADJ
positions NOUN
. PUNCT
This PRON
results VERB
to ADP
good ADJ
and CCONJ
capable ADJ
people NOUN
leaving VERB
the DET
company NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
frankfurt PROPN
is AUX
too ADV
big ADJ
to PART
live VERB
in ADP
. PUNCT
keep VERB
working VERB
life NOUN
balance NOUN
. PUNCT
need VERB
to PART
hard ADJ
to PART
suvirvework VERB
. PUNCT
missing VERB
the DET
green ADJ
world NOUN
in ADP
south PROPN
germany PROPN
Named Entity Recognition:
frankfurt GPE
south germany GPE
--------------------------------------------------
Tokenization and POS Tagging:
big ADJ
firm NOUN
, PUNCT
sometimes ADV
processes NOUN
are AUX
slow ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
Lower ADJ
pay NOUN
compared VERB
to ADP
other ADJ
locations NOUN
SPACE
* PUNCT
Less ADJ
career NOUN
opportunities NOUN
compared VERB
to ADP
other ADJ
locations NOUN
--------------------------------------------------
# Load the pre-trained spaCy model
nlp = spacy.load('en_core_web_md')
def process_text_with_spacy(text):
# Process the text with spaCy
doc = nlp(text)
# Tokenization and POS tagging
print("Tokenization and POS Tagging:")
for token in doc:
print(f"{token.text:{12}} {token.pos_:{10}}")
# Checking if there are named entities before printing
if doc.ents:
print("\nNamed Entity Recognition:")
for ent in doc.ents:
print(f"{ent.text:{17}} {ent.label_}")
print("\n" + "-"*50 + "\n")
# Applying the function to the first 5 'Pros' entries
print("Processing 'Pros':")
for n26_pros_text in n26['Pros']:
process_text_with_spacy(n26_pros_text)
# Applying the function to the first 5 'Cons' entries
print("Processing 'Cons':")
for n26_cons_text in n26['Cons']:
process_text_with_spacy(n26_cons_text)
Processing 'Pros':
Tokenization and POS Tagging:
Good ADJ
teams NOUN
and CCONJ
setups NOUN
, PUNCT
solid ADJ
Ways PROPN
of ADP
working VERB
Named Entity Recognition:
Ways of working ORG
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
environment NOUN
relaxed VERB
, PUNCT
even ADV
better ADV
after SCONJ
the DET
works PROPN
council PROPN
was AUX
entered VERB
( PUNCT
not PART
without ADP
some DET
tricky ADJ
avoidance NOUN
from ADP
top ADJ
managers NOUN
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
The DET
teams NOUN
have VERB
some PRON
of ADP
the DET
nicest ADJ
and CCONJ
hard ADJ
working VERB
people NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Diverse ADJ
work NOUN
, PUNCT
lot NOUN
's PART
of ADP
in ADP
- PUNCT
house NOUN
talent NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Wonderful ADJ
people NOUN
to PART
work VERB
with ADP
and CCONJ
state NOUN
of ADP
the DET
art NOUN
tech NOUN
stack NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
place NOUN
to PART
work VERB
in ADP
general ADJ
--------------------------------------------------
Tokenization and POS Tagging:
the DET
company NOUN
still ADV
is AUX
quite ADV
interesting ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
people NOUN
. PUNCT
Fun NOUN
product NOUN
. PUNCT
Learnt VERB
so ADV
much ADV
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
vibes NOUN
SPACE
good ADJ
peoples NOUN
SPACE
nice ADJ
office NOUN
in ADP
the DET
center NOUN
of ADP
berlin PROPN
SPACE
work NOUN
from ADP
home NOUN
Named Entity Recognition:
berlin GPE
--------------------------------------------------
Tokenization and POS Tagging:
Full ADJ
Remote PROPN
work NOUN
. PUNCT
The DET
product NOUN
analytics NOUN
team NOUN
is AUX
a DET
nice ADJ
and CCONJ
supportive ADJ
team NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
-Company NOUN
is AUX
fairly ADV
diverse ADJ
which PRON
is AUX
really ADV
interesting ADJ
. PUNCT
Lots NOUN
of ADP
professionals NOUN
from ADP
different ADJ
countries NOUN
SPACE
-Hybrid PROPN
work NOUN
environment NOUN
SPACE
-Personal PROPN
Development PROPN
Budget PROPN
( PUNCT
you PRON
can AUX
use VERB
it PRON
for ADP
courses NOUN
, PUNCT
bootcamps NOUN
etc X
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
flexible ADJ
working NOUN
conditions NOUN
and CCONJ
relatively ADV
fair ADJ
salary NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
competitive ADJ
salary NOUN
SPACE
- PUNCT
autonomy NOUN
and CCONJ
flexibility NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Good ADJ
growing VERB
opportunities NOUN
SPACE
- PUNCT
Good ADJ
starting VERB
salary NOUN
SPACE
- PUNCT
Good ADJ
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
experience NOUN
gainnig ADJ
, PUNCT
one NUM
weekly ADJ
lunch NOUN
and CCONJ
a DET
pizza NOUN
Friday PROPN
Named Entity Recognition:
gainnig PERSON
one CARDINAL
weekly DATE
Friday DATE
--------------------------------------------------
Tokenization and POS Tagging:
N26 PROPN
provides VERB
atmosphere NOUN
if SCONJ
you PRON
willing ADJ
to PART
grow VERB
, PUNCT
there PRON
's VERB
a DET
lot NOUN
of ADP
improvements NOUN
to PART
be AUX
done VERB
and CCONJ
team NOUN
is AUX
very ADV
welcome ADJ
for ADP
change NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
nice ADJ
atmospheres NOUN
and CCONJ
opportunities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
exposed VERB
to ADP
a DET
bank NOUN
in ADP
a DET
fast ADJ
moving VERB
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
proper ADJ
pay NOUN
( PUNCT
as ADP
for ADP
2022 NUM
) PUNCT
SPACE
- PUNCT
benefits NOUN
SPACE
- PUNCT
corporate ADJ
culture NOUN
and CCONJ
branding NOUN
SPACE
- PUNCT
team NOUN
Named Entity Recognition:
2022 DATE
--------------------------------------------------
Tokenization and POS Tagging:
Most ADJ
teams NOUN
in ADP
Platform PROPN
Engineering PROPN
segment NOUN
were AUX
full ADJ
of ADP
really ADV
good ADJ
people NOUN
, PUNCT
and CCONJ
managers NOUN
within ADP
the DET
segment NOUN
were AUX
competent ADJ
and CCONJ
empathetic ADJ
. PUNCT
SPACE
Work NOUN
was AUX
usually ADV
interesting ADJ
and CCONJ
there PRON
were VERB
opportunities NOUN
to PART
explore VERB
and CCONJ
adopt VERB
new ADJ
technologies NOUN
. PUNCT
SPACE
German ADJ
employees NOUN
have VERB
an DET
active ADJ
Works PROPN
Council PROPN
that PRON
strives VERB
to PART
improve VERB
working VERB
conditions NOUN
for ADP
employees NOUN
and CCONJ
make VERB
the DET
company NOUN
more ADV
successful ADJ
. PUNCT
Planned VERB
conversion NOUN
from ADP
AG PROPN
to ADP
SE PROPN
( PUNCT
Societas PROPN
Europas PROPN
) PUNCT
will AUX
provide VERB
similar ADJ
representation NOUN
for ADP
employees NOUN
in ADP
Spain PROPN
and CCONJ
other ADJ
EU PROPN
countries NOUN
. PUNCT
This PRON
is AUX
unusual ADJ
for ADP
a DET
tech NOUN
company NOUN
, PUNCT
but CCONJ
IMO ADV
a DET
good ADJ
thing NOUN
for ADP
N26 PROPN
and CCONJ
its PRON
employees NOUN
. PUNCT
Named Entity Recognition:
Platform Engineering ORG
German NORP
Works Council ORG
AG ORG
SE ORG
Spain GPE
EU ORG
--------------------------------------------------
Tokenization and POS Tagging:
long ADJ
hours NOUN
but CCONJ
worth ADJ
it PRON
Named Entity Recognition:
long hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
life NOUN
work NOUN
balance NOUN
. PUNCT
Modern ADJ
management NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
was AUX
able ADJ
to PART
apply VERB
myself PRON
to ADP
problems NOUN
that PRON
I PRON
cared VERB
about ADP
, PUNCT
learn VERB
a DET
lot NOUN
, PUNCT
and CCONJ
got VERB
to PART
enjoy VERB
delivering VERB
on ADP
some DET
good ADJ
challenges NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
work NOUN
culture NOUN
SPACE
Extremely ADV
focused VERB
leadership NOUN
across ADP
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Provides VERB
you PRON
with ADP
a DET
great ADJ
network NOUN
, PUNCT
but CCONJ
it PRON
's AUX
a DET
high ADJ
stress NOUN
environment NOUN
with ADP
many ADJ
manual ADJ
processes NOUN
--------------------------------------------------
Tokenization and POS Tagging:
When SCONJ
I PRON
was AUX
working VERB
at ADP
N26 PROPN
, PUNCT
I PRON
was AUX
honored VERB
to PART
be AUX
offered VERB
as ADP
many ADJ
opportunities NOUN
as SCONJ
I PRON
did AUX
given VERB
I PRON
did AUX
n't PART
have VERB
a DET
great ADJ
CV NOUN
or CCONJ
university NOUN
degree NOUN
prior ADV
to ADP
working VERB
at ADP
N26 PROPN
. PUNCT
SPACE
Leaders NOUN
were AUX
always ADV
open ADJ
to PART
give VERB
me PRON
a DET
shot NOUN
and CCONJ
see VERB
how SCONJ
I PRON
do VERB
, PUNCT
if SCONJ
I PRON
was AUX
willing ADJ
and CCONJ
interested ADJ
in ADP
progressing VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
High ADJ
likelihood NOUN
to PART
find VERB
likeminded ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
product NOUN
stands VERB
out ADP
as ADV
exceptional ADJ
and CCONJ
easily ADV
accessible ADJ
. PUNCT
Within ADP
the DET
team NOUN
, PUNCT
there PRON
are VERB
competent ADJ
individuals NOUN
among ADP
both DET
direct ADJ
team NOUN
members NOUN
and CCONJ
stakeholders NOUN
, PUNCT
fostering VERB
opportunities NOUN
to PART
build VERB
connections NOUN
beyond ADP
the DET
confines NOUN
of ADP
professional ADJ
relationships NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
modern ADJ
start VERB
up ADP
environment NOUN
in ADP
financial ADJ
services NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Corporate ADJ
benefits NOUN
( PUNCT
development NOUN
budget NOUN
, PUNCT
public ADJ
transport NOUN
ticket NOUN
, PUNCT
home NOUN
office NOUN
budget NOUN
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Flat ADJ
hierarchy NOUN
. PUNCT
Easy ADJ
to PART
approche VERB
the DET
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
life NOUN
balance NOUN
- PUNCT
flexibility NOUN
- PUNCT
good ADJ
benefits NOUN
- PUNCT
great ADJ
work NOUN
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
great ADJ
team NOUN
members NOUN
to PART
work VERB
with ADP
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
people NOUN
Growth PROPN
opportunities NOUN
Nice PROPN
offices NOUN
Opportunity NOUN
for ADP
change NOUN
--------------------------------------------------
Tokenization and POS Tagging:
the DET
pay NOUN
is AUX
quite ADV
alright ADJ
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
've AUX
been AUX
working VERB
at ADP
N26 PROPN
for ADP
2 NUM
years NOUN
and CCONJ
had VERB
all DET
the DET
support NOUN
I PRON
needed VERB
to PART
grow VERB
. PUNCT
I PRON
am AUX
doing VERB
an DET
internal ADJ
change NOUN
to ADP
another DET
team NOUN
to PART
keep VERB
on ADP
learning VERB
and CCONJ
keep VERB
on ADP
developing VERB
my PRON
career NOUN
. PUNCT
I PRON
like VERB
that SCONJ
I PRON
can AUX
have VERB
a DET
lot NOUN
of ADP
impact NOUN
at ADP
N26 PROPN
and CCONJ
always ADV
keep VERB
on ADP
improving VERB
things NOUN
:) PUNCT
Named Entity Recognition:
2 years DATE
--------------------------------------------------
Tokenization and POS Tagging:
Base ADJ
salary NOUN
Remote NOUN
policy NOUN
Flexibility NOUN
Autonomy PROPN
--------------------------------------------------
Tokenization and POS Tagging:
Nice ADJ
perks NOUN
, PUNCT
Great ADJ
offices NOUN
and CCONJ
very ADV
nice ADJ
team NOUN
mates NOUN
. PUNCT
Looking VERB
for ADP
some DET
positive ADJ
trend NOUN
ahead ADV
, PUNCT
at ADP
the DET
end NOUN
N26 PROPN
app NOUN
is AUX
one NUM
of ADP
the DET
best ADJ
in ADP
fintech NOUN
space NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
people NOUN
were AUX
cool ADJ
but CCONJ
most ADJ
of ADP
them PRON
already ADV
got AUX
laid VERB
off ADP
, PUNCT
the DET
company NOUN
name NOUN
is AUX
still ADV
good ADJ
in ADP
your PRON
CV NOUN
but CCONJ
it PRON
’s VERB
a DET
place NOUN
to PART
stay VERB
for ADP
a DET
bit NOUN
and CCONJ
then ADV
leave VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
learning NOUN
curve NOUN
, PUNCT
good ADJ
talent NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Amazing ADJ
mix NOUN
of ADP
people NOUN
Great ADJ
office NOUN
Great ADJ
spirit NOUN
Highly ADV
ambitious ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
enviroment NOUN
, PUNCT
possibilities NOUN
to PART
grow VERB
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Flexibility NOUN
- PUNCT
Environment PROPN
- PUNCT
Team PROPN
spirit NOUN
- PUNCT
Workload PROPN
- PUNCT
Events NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
work NOUN
environment NOUN
, PUNCT
nice ADJ
people NOUN
, PUNCT
Great ADJ
flexibility NOUN
at ADP
work NOUN
--------------------------------------------------
Tokenization and POS Tagging:
1 X
. PUNCT
Good ADJ
work NOUN
culture NOUN
and CCONJ
very ADV
helpful ADJ
set NOUN
of ADP
engineers NOUN
and CCONJ
leaders NOUN
2 X
. PUNCT
Reasonable ADJ
employee NOUN
benefits VERB
3 NUM
. PUNCT
Streamlined ADJ
process NOUN
in ADP
running VERB
the DET
project NOUN
4 NUM
. PUNCT
Open ADJ
feedback NOUN
systems NOUN
Named Entity Recognition:
1 CARDINAL
2 CARDINAL
3 CARDINAL
4 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Flexible ADJ
young ADJ
environment NOUN
, PUNCT
inclusive ADJ
and CCONJ
international ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Flat ADJ
hierarchy NOUN
, PUNCT
steep ADJ
learning NOUN
curve NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
WFM NOUN
- PUNCT
Dynamic ADJ
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Amazing ADJ
people NOUN
to PART
work VERB
with ADP
and CCONJ
diverse ADJ
colleagues NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
product NOUN
is AUX
nice ADJ
and CCONJ
some PRON
of ADP
benefits NOUN
, PUNCT
eg PROPN
BVG PROPN
ticket NOUN
, PUNCT
Germna PROPN
classes NOUN
were AUX
super ADV
good ADJ
. PUNCT
Named Entity Recognition:
BVG ORG
--------------------------------------------------
Tokenization and POS Tagging:
A DET
lot NOUN
of ADP
processes NOUN
to PART
learn VERB
from ADP
--------------------------------------------------
Tokenization and POS Tagging:
Young PROPN
Environment PROPN
, PUNCT
Perfect PROPN
Work PROPN
- PUNCT
Life NOUN
Balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Remote ADJ
work NOUN
, PUNCT
very ADV
warm ADJ
product NOUN
and CCONJ
tech NOUN
community NOUN
, PUNCT
good ADJ
work NOUN
/ SYM
life NOUN
balance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
onboarding NOUN
process NOUN
was AUX
very ADV
smooth ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Overall ADJ
great ADJ
experience NOUN
working VERB
at ADP
N26 PROPN
. PUNCT
Friendly ADJ
atmosphere NOUN
most ADJ
of ADP
the DET
time NOUN
. PUNCT
Flexible ADJ
working NOUN
hours NOUN
. PUNCT
Personal ADJ
development NOUN
budget NOUN
. PUNCT
Culture NOUN
of ADP
helping VERB
each DET
other ADJ
within ADP
the DET
team NOUN
. PUNCT
Great ADJ
compensation NOUN
package NOUN
. PUNCT
Named Entity Recognition:
Flexible working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
place NOUN
to PART
start VERB
your PRON
career NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
team NOUN
leaders NOUN
and CCONJ
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
culture NOUN
, PUNCT
people NOUN
is AUX
extremely ADV
nice ADJ
and CCONJ
helpful ADJ
. PUNCT
Great ADJ
place NOUN
to PART
learn VERB
and CCONJ
grow VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Product NOUN
and CCONJ
industry NOUN
have VERB
many ADJ
untapped ADJ
problem NOUN
space NOUN
to PART
deliver VERB
substantial ADJ
impact NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Working PROPN
Culture PROPN
within ADP
Teams PROPN
is AUX
amazing ADJ
Named Entity Recognition:
Working Culture within Teams ORG
--------------------------------------------------
Tokenization and POS Tagging:
Love VERB
the DET
opportunities NOUN
we PRON
are AUX
getting VERB
. PUNCT
Interesting ADJ
problems NOUN
to PART
solve VERB
and CCONJ
great ADJ
colleagues NOUN
Named Entity Recognition:
Love WORK_OF_ART
--------------------------------------------------
Tokenization and POS Tagging:
Working NOUN
environment NOUN
, PUNCT
leadership NOUN
, PUNCT
interesting ADJ
work NOUN
, PUNCT
decent ADJ
bens NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Extremely ADV
talented ADJ
professionals NOUN
across ADP
the DET
company NOUN
- PUNCT
High ADJ
quality NOUN
software NOUN
and CCONJ
principles NOUN
- PUNCT
Nice ADJ
benefits NOUN
, PUNCT
but CCONJ
there PRON
's VERB
also ADV
room NOUN
for ADP
improvement NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Company NOUN
dynamics NOUN
, PUNCT
community NOUN
, PUNCT
and CCONJ
perks NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
really ADV
in ADP
comparison NOUN
to ADP
other ADJ
places NOUN
. PUNCT
normal ADJ
salary NOUN
normal ADJ
benefits NOUN
. PUNCT
just ADV
a DET
place NOUN
to PART
work VERB
for ADP
short ADJ
time NOUN
and CCONJ
jump VERB
to ADP
other ADJ
places NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Fast ADV
- PUNCT
paced VERB
environment NOUN
with ADP
lots NOUN
of ADP
chances NOUN
to PART
make VERB
a DET
real ADJ
impact NOUN
. PUNCT
The DET
people NOUN
are AUX
nice ADJ
, PUNCT
team NOUN
leads VERB
tend VERB
to PART
think VERB
too ADV
much ADJ
of ADP
themselves PRON
but CCONJ
the DET
average ADJ
folk NOUN
is AUX
fine ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Stable ADJ
salary NOUN
Great ADJ
team NOUN
Name NOUN
Offer PROPN
relocation NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
much ADJ
pros NOUN
left VERB
there ADV
to PART
be AUX
honest ADJ
. PUNCT
I PRON
can AUX
not PART
think VERB
of ADP
anything PRON
. PUNCT
Maybe ADV
salary NOUN
is AUX
more ADJ
than ADP
average ADJ
but CCONJ
the DET
pressure NOUN
is AUX
so ADV
much ADJ
it PRON
does AUX
not PART
worth VERB
it PRON
. PUNCT
It PRON
's AUX
like ADP
living VERB
in ADP
a DET
high ADJ
paying VERB
3rd ADJ
world NOUN
country NOUN
. PUNCT
It PRON
's AUX
paying VERB
good NOUN
, PUNCT
but CCONJ
in ADP
a DET
dictatorship NOUN
. PUNCT
Named Entity Recognition:
3rd ORDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Colleagues NOUN
, PUNCT
perks NOUN
, PUNCT
transport NOUN
card NOUN
, PUNCT
fruits NOUN
, PUNCT
laptop NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
very ADV
dedicated ADJ
, PUNCT
helpful ADJ
colleagues NOUN
. PUNCT
SPACE
Some DET
interesting ADJ
projects NOUN
and CCONJ
customer NOUN
focused VERB
initiatives NOUN
. PUNCT
SPACE
A DET
new ADJ
office NOUN
with ADP
hopefully ADV
working VERB
air NOUN
conditioning NOUN
- PUNCT
a DET
potential ADJ
upgrade NOUN
from ADP
the DET
previous ADJ
" PUNCT
storage NOUN
space NOUN
" PUNCT
offices NOUN
. PUNCT
SPACE
Relatively ADV
ok ADJ
salary NOUN
compared VERB
to ADP
the DET
" PUNCT
IT NOUN
sector NOUN
" PUNCT
, PUNCT
but CCONJ
quite ADV
poor ADJ
when SCONJ
compared VERB
to ADP
the DET
financial ADJ
sector NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Flexibility NOUN
and CCONJ
Remote PROPN
work NOUN
is AUX
great ADJ
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
High ADJ
Pay NOUN
- PUNCT
Stress NOUN
free ADJ
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
N26 PROPN
is AUX
a DET
great ADJ
company NOUN
to PART
work VERB
for ADP
, PUNCT
especially ADV
as ADP
a DET
Sr PROPN
. PROPN
TDM NOUN
. PUNCT
The DET
environment NOUN
is AUX
positive ADJ
and CCONJ
supportive ADJ
, PUNCT
and CCONJ
management NOUN
truly ADV
empowers VERB
you PRON
to PART
take VERB
full ADJ
control NOUN
of ADP
the DET
product NOUN
or CCONJ
program NOUN
you PRON
're AUX
leading VERB
. PUNCT
This DET
level NOUN
of ADP
trust NOUN
is AUX
rare ADJ
in ADP
many ADJ
companies NOUN
, PUNCT
but CCONJ
at ADP
N26 PROPN
it PRON
's AUX
the DET
norm NOUN
. PUNCT
Additionally ADV
, PUNCT
management NOUN
is AUX
transparent ADJ
about ADP
how SCONJ
and CCONJ
why SCONJ
decisions NOUN
are AUX
made VERB
, PUNCT
which PRON
helps VERB
everyone PRON
feel VERB
like SCONJ
they PRON
're AUX
part NOUN
of ADP
the DET
process NOUN
. PUNCT
One NUM
of ADP
the DET
best ADJ
things NOUN
about ADP
working VERB
at ADP
N26 PROPN
is AUX
the DET
open ADJ
environment NOUN
where SCONJ
everyone PRON
can AUX
share VERB
their PRON
opinions NOUN
and CCONJ
concerns NOUN
. PUNCT
As ADP
a DET
Sr PROPN
. PROPN
TDM PROPN
, PUNCT
I PRON
feel VERB
like SCONJ
my PRON
contributions NOUN
are AUX
valued VERB
and CCONJ
directly ADV
contribute VERB
to ADP
N26 PROPN
's PART
growth NOUN
. PUNCT
I PRON
highly ADV
recommend VERB
N26 PROPN
as ADP
a DET
great ADJ
place NOUN
to PART
work VERB
, PUNCT
especially ADV
if SCONJ
you PRON
're AUX
passionate ADJ
about ADP
fintech NOUN
and CCONJ
want VERB
to PART
work VERB
in ADP
a DET
supportive ADJ
and CCONJ
transparent ADJ
environment NOUN
. PUNCT
Named Entity Recognition:
One CARDINAL
N26 ORG
--------------------------------------------------
Tokenization and POS Tagging:
The DET
tech NOUN
, PUNCT
great ADJ
office NOUN
culture NOUN
( PUNCT
snacks NOUN
, PUNCT
various ADJ
subsidies NOUN
etc ADJ
) PUNCT
and CCONJ
the DET
people NOUN
, PUNCT
good ADJ
growth NOUN
opportunities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
flat ADJ
hierarchy NOUN
( PUNCT
everyone PRON
has AUX
equal ADJ
voice NOUN
) PUNCT
- PUNCT
mutual ADJ
trust NOUN
- PUNCT
functional ADJ
work NOUN
council NOUN
- PUNCT
friendly ADJ
colleagues NOUN
- PUNCT
constant ADJ
learning NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
salaries NOUN
, PUNCT
modern ADJ
tools NOUN
and CCONJ
technologies NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Paycheck NOUN
punctual NOUN
at ADP
the DET
end NOUN
of ADP
the DET
month NOUN
- PUNCT
Good ADJ
office NOUN
facilities NOUN
- PUNCT
Successful PROPN
Product NOUN
Named Entity Recognition:
the end of the month - Good DATE
--------------------------------------------------
Tokenization and POS Tagging:
Growth NOUN
and CCONJ
hands NOUN
on ADP
mindset NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
place NOUN
to PART
work VERB
Friendly ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
cool ADJ
name NOUN
- PUNCT
great ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Nice ADJ
people NOUN
work VERB
there ADV
and CCONJ
free ADJ
lunch NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
smart ADJ
individuals NOUN
. PUNCT
Full ADJ
filling VERB
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Meaningful ADJ
Impact PROPN
on ADP
Projects NOUN
- PUNCT
Responsibilities NOUN
out ADP
of ADP
scope NOUN
- PUNCT
Huge ADJ
exposure NOUN
- PUNCT
Great ADJ
team NOUN
bonding NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
company NOUN
and CCONJ
culture NOUN
. PUNCT
I PRON
get AUX
done VERB
attitude NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
benefits NOUN
as ADP
remote ADJ
work NOUN
and CCONJ
bvg PROPN
subscription NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
work NOUN
- PUNCT
life NOUN
balance NOUN
. PUNCT
- PUNCT
good ADJ
team NOUN
- PUNCT
Diversity PROPN
and CCONJ
inclusion NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Work VERB
hard ADV
and CCONJ
it PRON
will AUX
not PART
go VERB
unnoticed ADJ
, PUNCT
a DET
learning NOUN
of ADP
really ADV
talented ADJ
people NOUN
to PART
learn VERB
from ADP
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Had VERB
a DET
good ADJ
experience NOUN
there ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Early ADJ
team NOUN
was AUX
incredible ADJ
– PUNCT
SPACE
collaborative ADJ
, PUNCT
hard ADV
- PUNCT
working VERB
, PUNCT
high ADJ
ownership NOUN
, PUNCT
compassionate ADJ
. PUNCT
These DET
people NOUN
became VERB
an DET
invaluable ADJ
network NOUN
for ADP
life NOUN
- PUNCT
Very ADV
strong ADJ
growth NOUN
curve NOUN
- PUNCT
Opportunity NOUN
to PART
work VERB
on ADP
interesting ADJ
topics NOUN
with ADP
high ADJ
customer NOUN
impact NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Environment NOUN
is AUX
quite ADV
healthy ADJ
and CCONJ
nice ADJ
place NOUN
to PART
grow VERB
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Ability NOUN
to PART
shape VERB
the DET
role NOUN
for ADP
yourself PRON
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
Well INTJ
structured ADJ
--------------------------------------------------
Tokenization and POS Tagging:
The DET
teams NOUN
are AUX
great ADJ
and CCONJ
i PRON
love VERB
to PART
work VERB
here ADV
! PUNCT
Flexible ADJ
Workplace PROPN
and CCONJ
homeoffice NOUN
makes VERB
it PRON
an DET
amazing ADJ
place NOUN
to PART
work VERB
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
people NOUN
management NOUN
, PUNCT
some DET
evolutions NOUN
opportunities NOUN
. PUNCT
Good ADJ
personal ADJ
development NOUN
budget NOUN
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
onboarding NOUN
and CCONJ
training NOUN
process NOUN
, PUNCT
nice ADJ
benefits NOUN
, PUNCT
flat ADJ
hierarchies NOUN
, PUNCT
nice ADJ
atmosphere NOUN
at ADP
work NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Payrise PROPN
came VERB
after ADP
1 NUM
year NOUN
Free PROPN
lunch NOUN
meal NOUN
once ADV
a DET
month NOUN
in ADP
- PUNCT
office NOUN
Flexible ADJ
with ADP
time NOUN
off ADP
for ADP
holiday NOUN
Great PROPN
Team PROPN
Leads VERB
Very ADV
good ADJ
with ADP
sickness NOUN
time NOUN
off ADP
Named Entity Recognition:
1 year DATE
Great Team Leads Very EVENT
--------------------------------------------------
Tokenization and POS Tagging:
nice ADJ
work NOUN
environment NOUN
, PUNCT
nice ADJ
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
to PART
be AUX
mentioned VERB
at ADV
all ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
work NOUN
from ADP
home NOUN
, PUNCT
no DET
micromanaging NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Working VERB
for ADP
N26 PROPN
as ADP
a DET
Working PROPN
Student PROPN
was AUX
a DET
very ADV
nice ADJ
experience NOUN
. PUNCT
Besides SCONJ
making VERB
it PRON
easy ADJ
to PART
study VERB
next ADV
to ADP
the DET
work NOUN
by ADP
facilitating VERB
a DET
very ADV
positive ADJ
and CCONJ
open ADJ
atmosphere NOUN
, PUNCT
I PRON
have AUX
learned VERB
a DET
lot NOUN
of ADP
workplace NOUN
fundamentals NOUN
from ADP
this DET
place NOUN
. PUNCT
One PRON
could AUX
always ADV
ask VERB
for ADP
support NOUN
, PUNCT
one PRON
would AUX
always ADV
get VERB
tasks NOUN
to PART
challenge VERB
oneself PRON
but CCONJ
was AUX
guided VERB
if SCONJ
needed VERB
to PART
. PUNCT
A DET
very ADV
good ADJ
workplace NOUN
for ADP
development NOUN
. PUNCT
Named Entity Recognition:
Working for N26 ORG
Working Student ORG
One CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
life NOUN
balance NOUN
, PUNCT
Team NOUN
support NOUN
, PUNCT
work NOUN
environment NOUN
, PUNCT
Job PROPN
security NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
chance NOUN
to PART
develop VERB
within ADP
the DET
company NOUN
--------------------------------------------------
Tokenization and POS Tagging:
horizontal ADJ
hierarchy NOUN
many ADJ
tasks NOUN
for ADP
an DET
intern NOUN
learned VERB
a DET
lot NOUN
about ADP
AFC PROPN
AML PROPN
Topics PROPN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
feel VERB
my PRON
job NOUN
is AUX
really ADV
valued VERB
here ADV
and CCONJ
I PRON
could AUX
say VERB
that SCONJ
N26 PROPN
's PART
employees NOUN
have VERB
voice NOUN
and CCONJ
are AUX
heard VERB
. PUNCT
All DET
the DET
guidelines NOUN
regarding VERB
promotions NOUN
are AUX
transparent ADJ
and CCONJ
clearly ADV
established VERB
so ADV
you PRON
know VERB
what PRON
to PART
do VERB
and CCONJ
what PRON
to PART
expect VERB
, PUNCT
different ADJ
from ADP
other ADJ
places NOUN
where SCONJ
no DET
one NOUN
gives VERB
any DET
hint NOUN
about ADP
growth NOUN
possibilities NOUN
. PUNCT
There PRON
is VERB
also ADV
a DET
nice ADJ
thing NOUN
about ADP
collaboration NOUN
, PUNCT
I PRON
was AUX
always ADV
helped VERB
by ADP
people NOUN
from ADP
other ADJ
departments NOUN
when SCONJ
I PRON
needed VERB
. PUNCT
Named Entity Recognition:
N26 ORG
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
work NOUN
/ SYM
life NOUN
balance NOUN
, PUNCT
growth NOUN
opportunities NOUN
, PUNCT
good ADJ
culture NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Team PROPN
spirit NOUN
SPACE
Possibility PROPN
to PART
grow VERB
SPACE
Supportive PROPN
Management PROPN
SPACE
Location PROPN
SPACE
Employee NOUN
benefits NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Compensation NOUN
, PUNCT
work NOUN
- PUNCT
life NOUN
balance NOUN
, PUNCT
and CCONJ
a DET
very ADV
friendly ADJ
environment NOUN
. PUNCT
There PRON
are VERB
great ADJ
professionals NOUN
to PART
learn VERB
from ADP
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Benefits NOUN
to PART
keep VERB
you PRON
entertained ADJ
- PUNCT
pizza NOUN
, PUNCT
snacks NOUN
, PUNCT
drinks NOUN
, PUNCT
parties NOUN
, PUNCT
bean NOUN
bags NOUN
etc X
. PUNCT
Young ADJ
workforce NOUN
, PUNCT
good ADJ
for ADP
connections NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Great PROPN
Team PROPN
SPACE
Great PROPN
office NOUN
SPACE
Great ADJ
equipment NOUN
and CCONJ
benefits NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Culture PROPN
is AUX
pretty ADV
good ADJ
SPACE
- PUNCT
Work PROPN
Life PROPN
balance NOUN
is AUX
greatly ADV
maintained VERB
SPACE
- PUNCT
Stable ADJ
Company PROPN
with ADP
increasing VERB
growth NOUN
every DET
year NOUN
SPACE
- PUNCT
Almost ADV
the DET
latest ADJ
tech NOUN
stack NOUN
in ADP
the DET
market NOUN
SPACE
- PUNCT
Good ADJ
team NOUN
diversity NOUN
and CCONJ
your PRON
opinion NOUN
matters VERB
with ADP
the DET
product NOUN
Named Entity Recognition:
every year
- Almost DATE
--------------------------------------------------
Tokenization and POS Tagging:
1 X
. PUNCT
Lots NOUN
of ADP
opportunity NOUN
for ADP
growth NOUN
, PUNCT
autonomy NOUN
, PUNCT
and CCONJ
ownership NOUN
. PUNCT
You PRON
can AUX
really ADV
take VERB
a DET
project NOUN
and CCONJ
drive VERB
it PRON
forward ADV
to PART
end VERB
, PUNCT
though SCONJ
it PRON
will AUX
take VERB
a DET
lot NOUN
of ADP
persistence NOUN
. PUNCT
SPACE
2 X
. PUNCT
They PRON
stepped VERB
up ADP
their PRON
salary NOUN
bands NOUN
in ADP
the DET
last ADJ
year NOUN
or CCONJ
so ADV
making VERB
the DET
work NOUN
well ADV
work VERB
it PRON
SPACE
3 NUM
. PUNCT
Never ADV
bored VERB
, PUNCT
there PRON
’s VERB
always ADV
something PRON
to PART
create VERB
, PUNCT
change NOUN
, PUNCT
or CCONJ
fix VERB
. PUNCT
SPACE
4 X
. PUNCT
ELT PROPN
/ SYM
c PROPN
level NOUN
team NOUN
is AUX
competent ADJ
and CCONJ
I PRON
do AUX
trust VERB
them PRON
and CCONJ
their PRON
vision NOUN
SPACE
5 NUM
. PUNCT
The DET
majority NOUN
of ADP
my PRON
colleagues NOUN
are AUX
solid ADJ
people NOUN
who PRON
really ADV
want VERB
to PART
make VERB
things NOUN
better ADJ
and CCONJ
care VERB
about ADP
their PRON
jobs NOUN
. PUNCT
Named Entity Recognition:
1 CARDINAL
2 CARDINAL
the last year or so DATE
3 CARDINAL
4 CARDINAL
5 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Great ADJ
brand NOUN
SPACE
- PUNCT
Smart ADJ
people NOUN
SPACE
- PUNCT
Reasonable ADJ
salaries NOUN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
’ve AUX
been AUX
there ADV
for ADP
around ADP
2 NUM
years NOUN
and CCONJ
I PRON
can AUX
say VERB
that SCONJ
they PRON
really ADV
care VERB
about ADP
the DET
people NOUN
. PUNCT
I PRON
’ve AUX
seen VERB
so ADV
many ADJ
initiates NOUN
around ADP
improving VERB
the DET
work NOUN
life NOUN
and CCONJ
how SCONJ
to PART
do VERB
better ADV
. PUNCT
Named Entity Recognition:
around 2 years DATE
--------------------------------------------------
Tokenization and POS Tagging:
lovely ADJ
people NOUN
, PUNCT
benefits NOUN
could AUX
be AUX
stepped VERB
up ADP
--------------------------------------------------
Tokenization and POS Tagging:
People NOUN
are AUX
talented ADJ
and CCONJ
friendly ADJ
--------------------------------------------------
Tokenization and POS Tagging:
+ CCONJ
great ADJ
team NOUN
SPACE
+ SYM
supportive ADJ
management NOUN
SPACE
+ SYM
flexible ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Teams NOUN
and CCONJ
people NOUN
are AUX
awesome ADJ
, PUNCT
cool ADJ
product NOUN
, PUNCT
lots NOUN
of ADP
resources NOUN
to PART
do VERB
your PRON
job NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
team NOUN
, PUNCT
people NOUN
help VERB
each DET
other ADJ
and CCONJ
all DET
team NOUN
members NOUN
are AUX
very ADV
responsive ADJ
. PUNCT
There PRON
is VERB
mutual ADJ
respect NOUN
and CCONJ
cooperation NOUN
. PUNCT
Events NOUN
and CCONJ
team NOUN
building NOUN
give VERB
employees NOUN
the DET
opportunity NOUN
to PART
get VERB
to PART
know VERB
each DET
other ADJ
better ADV
. PUNCT
Good ADJ
working NOUN
conditions NOUN
have AUX
been AUX
created VERB
for ADP
people NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
The DET
culture NOUN
in ADP
the DET
product NOUN
team NOUN
is AUX
great ADJ
--------------------------------------------------
Tokenization and POS Tagging:
good ADJ
benefits NOUN
on ADP
self NOUN
development NOUN
SPACE
possibilities NOUN
for ADP
a DET
promotion NOUN
SPACE
supportive ADJ
teams NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
multicultural ADJ
environment NOUN
. PUNCT
Friendly ADJ
people NOUN
, PUNCT
a DET
typical ADJ
Berlin PROPN
startup NOUN
. PUNCT
Named Entity Recognition:
Berlin GPE
--------------------------------------------------
Tokenization and POS Tagging:
1 X
. PUNCT
Amazing ADJ
work NOUN
- PUNCT
life NOUN
balance NOUN
SPACE
2 NUM
. PUNCT
Appreciate VERB
the DET
standard NOUN
of ADP
work NOUN
aimed VERB
despite SCONJ
how SCONJ
much ADV
we PRON
can AUX
achieve VERB
SPACE
3 NUM
. PUNCT
Greate ADJ
people NOUN
, PUNCT
progressive ADJ
culture NOUN
SPACE
4 NUM
. PUNCT
Great ADJ
learning NOUN
opportunities NOUN
and CCONJ
support VERB
Named Entity Recognition:
1 CARDINAL
2 CARDINAL
3 CARDINAL
4 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Lots NOUN
of ADP
learning NOUN
, PUNCT
great ADJ
team NOUN
, PUNCT
bright ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
dynamic ADJ
, PUNCT
frenetic ADJ
good ADJ
salary NOUN
for ADP
all DET
the DET
positions NOUN
above ADP
the DET
cs PROPN
specialist ADJ
role NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Employee NOUN
friendly ADJ
overall ADJ
very ADV
good ADJ
--------------------------------------------------
Tokenization and POS Tagging:
technology NOUN
, PUNCT
problems NOUN
to PART
solve VERB
. PUNCT
Initiatives NOUN
. PUNCT
Stable ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Open ADJ
communication NOUN
style NOUN
in ADP
that SCONJ
you PRON
feel VERB
like SCONJ
you PRON
can AUX
be AUX
yourself PRON
with ADP
colleagues NOUN
SPACE
Colleagues NOUN
are AUX
friendly ADJ
and CCONJ
helpful ADJ
SPACE
If SCONJ
you PRON
're AUX
solution NOUN
- PUNCT
oriented VERB
and CCONJ
driven VERB
, PUNCT
you PRON
can AUX
go VERB
a DET
long ADJ
way NOUN
SPACE
Market NOUN
average ADJ
compensation NOUN
( PUNCT
after ADP
some DET
time NOUN
) PUNCT
SPACE
Flexible ADJ
work NOUN
situation NOUN
( PUNCT
after ADP
some DET
discussion NOUN
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Frist ADJ
weeks NOUN
are AUX
awesome ADJ
, PUNCT
a DET
lot NOUN
of ADP
welcoming VERB
people NOUN
and CCONJ
nice ADJ
environment NOUN
. PUNCT
Named Entity Recognition:
Frist weeks DATE
--------------------------------------------------
Tokenization and POS Tagging:
great ADJ
team NOUN
SPACE
good ADJ
processes NOUN
SPACE
a DET
lot NOUN
of ADP
people NOUN
who PRON
are AUX
talented ADJ
and CCONJ
you PRON
can AUX
learn VERB
from ADP
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
work NOUN
/ SYM
life NOUN
balance NOUN
. PUNCT
Nice ADJ
perks NOUN
--------------------------------------------------
Tokenization and POS Tagging:
flexible ADJ
hours NOUN
, PUNCT
location NOUN
, PUNCT
learning VERB
opportunities NOUN
, PUNCT
good ADJ
salary NOUN
--------------------------------------------------
Tokenization and POS Tagging:
International ADJ
staff NOUN
, PUNCT
opportunity NOUN
to PART
build VERB
great ADJ
relationships NOUN
with ADP
many ADJ
different ADJ
people NOUN
. PUNCT
Employee NOUN
perks NOUN
like ADP
free ADJ
lunches NOUN
, PUNCT
free ADJ
food NOUN
on ADP
special ADJ
occasions NOUN
etc X
. X
Can AUX
grow VERB
a DET
lot NOUN
professionally ADV
and CCONJ
get VERB
great ADJ
experience NOUN
if SCONJ
you PRON
are AUX
motivated ADJ
and CCONJ
self NOUN
sufficient ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Happy ADJ
working VERB
here ADV
I PRON
guess VERB
--------------------------------------------------
Tokenization and POS Tagging:
Free ADJ
drinks NOUN
and CCONJ
that PRON
's AUX
all PRON
--------------------------------------------------
Tokenization and POS Tagging:
Many ADJ
benefits NOUN
like ADP
Wednesday PROPN
lunch NOUN
, PUNCT
Friday PROPN
beers NOUN
, PUNCT
discount NOUN
in ADP
public ADJ
transport NOUN
. PUNCT
Named Entity Recognition:
Wednesday DATE
Friday DATE
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
really ADV
love VERB
the DET
culture NOUN
here ADV
. PUNCT
Of ADV
course ADV
, PUNCT
it PRON
always ADV
depends VERB
on ADP
the DET
team NOUN
, PUNCT
but CCONJ
I PRON
felt VERB
like ADP
home NOUN
since SCONJ
the DET
very ADJ
beginning NOUN
. PUNCT
SPACE
You PRON
have VERB
a DET
lot NOUN
of ADP
chances NOUN
to PART
grow VERB
and CCONJ
develop VERB
, PUNCT
take VERB
side NOUN
projects NOUN
and CCONJ
contribute VERB
to ADP
building NOUN
processes NOUN
. PUNCT
SPACE
It PRON
's AUX
a DET
very ADV
dynamic ADJ
and CCONJ
changing VERB
environment NOUN
, PUNCT
which PRON
can AUX
be AUX
challenging ADJ
, PUNCT
but CCONJ
at ADP
the DET
same ADJ
time NOUN
exciting ADJ
. PUNCT
SPACE
There PRON
are VERB
a DET
lot NOUN
of ADP
team NOUN
activities NOUN
and CCONJ
diversity NOUN
and CCONJ
inclusion NOUN
are AUX
not PART
just ADV
words NOUN
here ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Wonderful ADJ
experience NOUN
there ADV
, PUNCT
gained VERB
a DET
lot NOUN
of ADP
inputs NOUN
, PUNCT
met VERB
great ADJ
people NOUN
and CCONJ
got AUX
fairly ADV
paid VERB
--------------------------------------------------
Tokenization and POS Tagging:
N26 PROPN
on ADP
CV NOUN
is AUX
good ADJ
for ADP
career NOUN
at ADP
the DET
moment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Amazing ADJ
work NOUN
environment NOUN
SPACE
- PUNCT
Great ADJ
growth NOUN
opportunities NOUN
SPACE
- PUNCT
Attentive ADJ
managers NOUN
who PRON
care VERB
about ADP
teams NOUN
and CCONJ
support NOUN
SPACE
- PUNCT
Infinite PROPN
learning NOUN
opportunities NOUN
SPACE
- PUNCT
You PRON
can AUX
work VERB
in ADP
one NUM
team NOUN
and CCONJ
be AUX
able ADJ
to PART
expand VERB
knowledge NOUN
about ADP
other ADJ
teams NOUN
and CCONJ
they PRON
ways NOUN
of ADP
working VERB
as ADV
well ADV
SPACE
- PUNCT
Internal ADJ
moves NOUN
possibilities NOUN
SPACE
- PUNCT
Everyone PRON
's PART
ideas NOUN
and CCONJ
opinion NOUN
are AUX
appreciated VERB
and CCONJ
can AUX
be AUX
implicated VERB
SPACE
- PUNCT
Freedom PROPN
for ADP
creativity NOUN
Named Entity Recognition:
one CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
N26 PROPN
is AUX
entering VERB
a DET
new ADJ
phase NOUN
of ADP
its PRON
development NOUN
, PUNCT
professionalising VERB
itself PRON
in ADP
all DET
areas NOUN
and CCONJ
expanding VERB
its PRON
teams NOUN
with ADP
great ADJ
minds NOUN
coming VERB
from ADP
all ADV
over ADP
the DET
world NOUN
. PUNCT
SPACE
- PUNCT
N26 PROPN
's PART
leadership NOUN
team NOUN
is AUX
stabilising VERB
after ADP
a DET
period NOUN
of ADP
high ADJ
turnover NOUN
and CCONJ
lots NOUN
of ADP
great ADJ
profiles NOUN
have AUX
been AUX
added VERB
to ADP
the DET
team NOUN
over ADP
the DET
past ADJ
months NOUN
which PRON
highly ADV
influences VERB
the DET
strategy NOUN
for ADP
the DET
company NOUN
and CCONJ
for ADP
the DET
people NOUN
. PUNCT
SPACE
- PUNCT
Very ADV
competitive ADJ
compensation NOUN
& CCONJ
rewards NOUN
package NOUN
with ADP
tremendous ADJ
opportunities NOUN
to PART
learn VERB
and CCONJ
grow VERB
. PUNCT
SPACE
- PUNCT
Offices NOUN
in ADP
the DET
heart NOUN
of ADP
Berlin PROPN
with ADP
lots NOUN
of ADP
additional ADJ
perks NOUN
and CCONJ
benefits NOUN
. PUNCT
Named Entity Recognition:
the past months DATE
Berlin GPE
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
culture NOUN
SPACE
- PUNCT
colleagues NOUN
SPACE
- PUNCT
leadership NOUN
SPACE
- PUNCT
benefits NOUN
SPACE
- PUNCT
career NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
benefits NOUN
and CCONJ
salary NOUN
provided VERB
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
people NOUN
to PART
work VERB
with ADP
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
nice ADJ
and CCONJ
talented ADJ
coworkers NOUN
SPACE
Nice PROPN
office NOUN
Named Entity Recognition:
Nice GPE
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
High ADJ
degree NOUN
of ADP
autonomy NOUN
SPACE
- PUNCT
Steep ADJ
learning NOUN
curve NOUN
SPACE
- PUNCT
Good ADJ
team NOUN
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
place NOUN
to PART
learn VERB
and CCONJ
develop VERB
, PUNCT
great ADJ
peers NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
company NOUN
offers VERB
a DET
clear ADJ
path NOUN
for ADP
career NOUN
growth NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Dynamic ADJ
work NOUN
environment NOUN
SPACE
good ADJ
location NOUN
SPACE
smart ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
working VERB
environment NOUN
, PUNCT
excellent ADJ
managers NOUN
and CCONJ
amazing ADJ
scope NOUN
of ADP
growth NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
learning NOUN
job NOUN
position NOUN
: PUNCT
SPACE
- PUNCT
Good ADJ
engineering NOUN
culture NOUN
inherited VERB
from ADP
thoughtworks NOUN
SPACE
- PUNCT
Talented VERB
leads NOUN
and CCONJ
seniors NOUN
to PART
look VERB
up ADP
to ADP
--------------------------------------------------
Tokenization and POS Tagging:
like ADP
every DET
other ADJ
startup NOUN
, PUNCT
good ADJ
atmosphere NOUN
between ADP
peers NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Flexible ADJ
work NOUN
hours NOUN
and CCONJ
Diverse ADJ
teams NOUN
Named Entity Recognition:
Flexible work hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
opportunity NOUN
to PART
work VERB
every DET
day NOUN
and CCONJ
some DET
great ADJ
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
People NOUN
, PUNCT
Groupwork PROPN
and CCONJ
projects NOUN
and CCONJ
environment NOUN
Named Entity Recognition:
People, Groupwork ORG
--------------------------------------------------
Tokenization and POS Tagging:
Many ADJ
advantages NOUN
that PRON
are AUX
constantly ADV
changing VERB
like ADP
a DET
budget NOUN
to PART
study VERB
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
creative ADJ
SPACE
- PUNCT
challenging VERB
SPACE
- PUNCT
support NOUN
from ADP
all DET
teams NOUN
--------------------------------------------------
Tokenization and POS Tagging:
This PRON
is AUX
still ADV
a DET
startup NOUN
. PUNCT
A DET
successful ADJ
one NOUN
, PUNCT
in ADP
later ADJ
stages NOUN
of ADP
growth NOUN
( PUNCT
after ADP
hypergrowth NOUN
phase NOUN
) PUNCT
, PUNCT
on ADP
the DET
way NOUN
to ADP
IPO PROPN
. PUNCT
Requires VERB
real ADV
hard ADJ
work NOUN
and CCONJ
bigger ADJ
- PUNCT
scale NOUN
experience NOUN
. PUNCT
If SCONJ
you PRON
look VERB
for ADP
a DET
happy ADJ
20 NUM
ppl PROPN
everyone PRON
- PUNCT
knows VERB
- PUNCT
everyone PRON
setup NOUN
this PRON
is AUX
not PART
for ADP
you PRON
. PUNCT
If SCONJ
you PRON
want VERB
to PART
learn VERB
how SCONJ
engineering NOUN
org NOUN
with ADP
300 NUM
ppl NOUN
, PUNCT
tens NOUN
of ADP
microservices NOUN
with ADP
modern ADJ
Ci PROPN
/ SYM
CD PROPN
pipeline NOUN
work NOUN
while SCONJ
keeping VERB
compliance NOUN
, PUNCT
reliability NOUN
and CCONJ
automomy NOUN
in ADP
place NOUN
you PRON
should AUX
join VERB
. PUNCT
A DET
lot NOUN
to PART
learn VERB
( PUNCT
also ADV
some DET
bad ADJ
examples NOUN
:) PUNCT
. PUNCT
Great ADJ
engineers NOUN
and CCONJ
friends NOUN
. PUNCT
Named Entity Recognition:
hypergrowth LANGUAGE
IPO ORG
20 CARDINAL
300 CARDINAL
tens CARDINAL
Ci PERSON
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Fast ADV
- PUNCT
paced VERB
SPACE
- PUNCT
Ability NOUN
to PART
learn VERB
quickly ADV
SPACE
- PUNCT
Great ADJ
people NOUN
SPACE
- PUNCT
High ADJ
autonomy NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
Innovative ADJ
place NOUN
to PART
work VERB
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
teammates NOUN
and CCONJ
very ADV
interesting ADJ
problem NOUN
space NOUN
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
Great ADJ
product NOUN
SPACE
* PUNCT
Great ADJ
people NOUN
- PUNCT
co NOUN
- NOUN
workers NOUN
SPACE
* PUNCT
One PRON
can AUX
pick VERB
an DET
initiative NOUN
and CCONJ
take VERB
it PRON
from ADP
start NOUN
to ADP
finish NOUN
. PUNCT
Persistence NOUN
required VERB
but CCONJ
eventually ADV
I PRON
can AUX
make VERB
things NOUN
happen VERB
. PUNCT
SPACE
* PUNCT
Tech NOUN
side NOUN
of ADP
things NOUN
is AUX
modern ADJ
with ADP
strong ADJ
emphasis NOUN
on ADP
quality NOUN
, PUNCT
security NOUN
SPACE
* PUNCT
I PRON
also ADV
really ADV
like ADP
the DET
attention NOUN
to ADP
detail NOUN
in ADP
design NOUN
so SCONJ
we PRON
make VERB
a DET
product NOUN
with ADP
focus NOUN
on ADP
customer NOUN
use NOUN
cases NOUN
SPACE
* PUNCT
Compensation NOUN
got VERB
much ADV
better ADJ
lately ADV
--------------------------------------------------
Tokenization and POS Tagging:
Environment NOUN
where SCONJ
to PART
have VERB
huge ADJ
growth NOUN
opportunities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Product NOUN
is AUX
good ADJ
SPACE
Personal ADJ
dev NOUN
budget NOUN
( PUNCT
1500€/year ADJ
) PUNCT
SPACE
Benefits NOUN
SPACE
Startup PROPN
company NOUN
and CCONJ
easy ADJ
to PART
grow VERB
with ADP
Named Entity Recognition:
1500€/year CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Nice ADJ
coworkers NOUN
and CCONJ
decent ADJ
salaries NOUN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
really ADV
enjoyed VERB
working VERB
at ADP
N26 PROPN
for ADP
my PRON
time NOUN
there ADV
. PUNCT
The DET
direct ADJ
team NOUN
is AUX
often ADV
great ADJ
, PUNCT
as ADV
well ADV
as ADP
the DET
direct ADJ
managers NOUN
, PUNCT
that PRON
are AUX
often ADV
very ADV
talented ADJ
and CCONJ
with ADP
a DET
lot NOUN
to PART
learn VERB
from ADP
. PUNCT
SPACE
Despite SCONJ
the DET
negative ADJ
sides NOUN
I PRON
would AUX
still ADV
recommend VERB
it PRON
as ADP
a DET
place NOUN
to PART
work VERB
, PUNCT
given VERB
that SCONJ
they PRON
fixed VERB
the DET
low ADJ
salaries NOUN
for ADP
the DET
design NOUN
positions NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
on ADP
- PUNCT
market NOUN
salary NOUN
, PUNCT
free ADJ
lunch NOUN
once SCONJ
a DET
week NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
fun ADJ
time NOUN
with ADP
smart ADJ
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
great ADJ
team NOUN
/ SYM
colleagues NOUN
SPACE
start VERB
up ADP
environment NOUN
SPACE
lots NOUN
of ADP
career NOUN
opportunities NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Colleagues NOUN
and CCONJ
paid VERB
BVG PROPN
card NOUN
are AUX
the DET
best ADJ
thing NOUN
about ADP
N26 PROPN
. PUNCT
Named Entity Recognition:
BVG ORG
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
place NOUN
to PART
work VERB
with ADP
pwoplw PROPN
Named Entity Recognition:
pwoplw ORG
--------------------------------------------------
Tokenization and POS Tagging:
Collaboration NOUN
, PUNCT
People PROPN
, PUNCT
Perks PROPN
, PUNCT
Time PROPN
off ADP
Named Entity Recognition:
Time ORG
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Talented VERB
, PUNCT
driven VERB
colleagues NOUN
SPACE
- PUNCT
super ADV
strong ADJ
brand NOUN
, PUNCT
good ADJ
product NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
company NOUN
to PART
work VERB
, PUNCT
nice ADJ
work NOUN
environment NOUN
, PUNCT
a DET
lot NOUN
of ADP
freedom NOUN
to PART
decide VERB
stuff NOUN
, PUNCT
GSDD PROPN
, PUNCT
strong ADJ
name NOUN
to PART
have VERB
in ADP
your PRON
CV NOUN
, PUNCT
etc X
Named Entity Recognition:
GSDD ORG
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
colleagues NOUN
& CCONJ
location NOUN
of ADP
workplace NOUN
--------------------------------------------------
Tokenization and POS Tagging:
International ADJ
colleagues NOUN
, PUNCT
big ADJ
player NOUN
in ADP
the DET
industry NOUN
--------------------------------------------------
Tokenization and POS Tagging:
very ADV
few ADJ
. PUNCT
the DET
only ADJ
thing NOUN
I PRON
can AUX
think VERB
of ADP
is AUX
- PUNCT
well ADJ
nothing PRON
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Gives VERB
enough ADJ
decision NOUN
- PUNCT
making VERB
authority NOUN
SPACE
- PUNCT
Allows NOUN
to PART
be AUX
creative ADJ
SPACE
- PUNCT
Provides VERB
the DET
opportunity NOUN
to PART
learn VERB
SPACE
- PUNCT
Not PART
very ADV
political ADJ
SPACE
- PUNCT
Enjoyable ADJ
place NOUN
to PART
work VERB
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
employee NOUN
interaction NOUN
, PUNCT
not PART
so ADV
much ADJ
due ADJ
to ADP
Corona PROPN
Named Entity Recognition:
Corona ORG
--------------------------------------------------
Tokenization and POS Tagging:
Good ADJ
tech NOUN
and CCONJ
use NOUN
to PART
have VERB
great ADJ
people NOUN
to PART
work VERB
with ADP
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Have AUX
made VERB
some DET
great ADJ
friends NOUN
--------------------------------------------------
Tokenization and POS Tagging:
( PUNCT
from ADP
the DET
start NOUN
of ADP
my PRON
time NOUN
at ADP
N26 PROPN
) PUNCT
SPACE
- PUNCT
Amazing ADJ
team NOUN
, PUNCT
some PRON
of ADP
the DET
smartest ADJ
and CCONJ
most ADV
motivated ADJ
people NOUN
I PRON
've AUX
ever ADV
worked VERB
with ADP
SPACE
- PUNCT
Tons NOUN
of ADP
opportunity NOUN
for ADP
growth NOUN
in ADP
your PRON
career NOUN
SPACE
- PUNCT
A DET
strong ADJ
brand NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
friends NOUN
in ADP
immediate ADJ
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Team NOUN
, PUNCT
flexible ADJ
working NOUN
environment NOUN
, PUNCT
making VERB
an DET
impact NOUN
( PUNCT
if SCONJ
you PRON
get VERB
cool ADJ
tasks NOUN
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Diverse ADJ
talents NOUN
from ADP
around ADP
the DET
world NOUN
, PUNCT
you PRON
can AUX
make VERB
a DET
difference NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Relocation NOUN
bonus NOUN
, PUNCT
visa NOUN
assistant NOUN
, PUNCT
international ADJ
environment NOUN
and CCONJ
new ADJ
office NOUN
. PUNCT
Recently ADV
they PRON
gave VERB
us PRON
a DET
raise NOUN
but CCONJ
it PRON
's AUX
too ADV
late ADJ
since SCONJ
most ADJ
team NOUN
members NOUN
who PRON
were AUX
actually ADV
doing VERB
work NOUN
and CCONJ
care VERB
about ADP
delivery NOUN
quality NOUN
had AUX
all PRON
left VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Possibilities NOUN
to PART
make VERB
a DET
difference NOUN
if SCONJ
you PRON
look VERB
for ADP
the DET
opportunities NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
dynamic ADJ
, PUNCT
busy ADJ
, PUNCT
responsibility NOUN
, PUNCT
innovative ADJ
, PUNCT
people NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Amazing ADJ
talented ADJ
colleagues NOUN
, PUNCT
an DET
experience NOUN
in ADP
the DET
Finntech PROPN
SPACE
Money NOUN
at ADP
the DET
end NOUN
of ADP
the DET
month NOUN
to PART
eat VERB
SPACE
Free ADJ
drinks NOUN
? PUNCT
Named Entity Recognition:
the Finntech
Money ORG
the end of the month DATE
--------------------------------------------------
Tokenization and POS Tagging:
The DET
people NOUN
are AUX
so ADV
dynamic ADJ
, PUNCT
however ADV
a DET
lot NOUN
of ADP
people NOUN
have AUX
left VERB
recently ADV
--------------------------------------------------
Tokenization and POS Tagging:
Flexible ADJ
, PUNCT
young ADJ
, PUNCT
amazing ADJ
people NOUN
with ADP
different ADJ
backgrounds NOUN
, PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Nice ADJ
people NOUN
SPACE
Opportunity PROPN
to PART
learn VERB
new ADJ
skills NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Always ADV
wanted VERB
to PART
work VERB
for ADP
a DET
startup NOUN
bank NOUN
! PUNCT
! PUNCT
Maybe ADV
one NUM
day NOUN
others NOUN
will AUX
believe VERB
it PRON
too ADV
! PUNCT
! PUNCT
! PUNCT
SPACE
Throughout ADP
the DET
last ADJ
few ADJ
years NOUN
I PRON
witnessed VERB
exponential ADJ
hypergrowth NOUN
, PUNCT
in ADP
the DET
sense NOUN
of ADP
new ADJ
blood NOUN
coming VERB
into ADP
the DET
meat NOUN
grinder NOUN
and CCONJ
old ADJ
discarded VERB
. PUNCT
At ADP
a DET
very ADV
calculated ADJ
interval NOUN
of ADP
6 NUM
- SYM
12 NUM
months NOUN
! PUNCT
SPACE
What PRON
a DET
gamechanger NOUN
! PUNCT
SPACE
Such DET
a DET
splendid ADJ
atmosphere NOUN
eating VERB
sugary ADJ
snacks NOUN
to PART
keep VERB
running VERB
in ADP
the DET
treadmill NOUN
! PUNCT
The DET
very ADV
little ADJ
benefits NOUN
and CCONJ
below ADP
market NOUN
pay NOUN
in ADP
engineering NOUN
keep VERB
my PRON
subordinates NOUN
very ADV
stimulated ADJ
. PUNCT
The DET
-1 ADJ
budget NOUN
for ADP
team NOUN
events NOUN
is AUX
an DET
added VERB
bonus NOUN
! PUNCT
SPACE
Loved VERB
working VERB
with ADP
burned VERB
- PUNCT
out ADP
engineers NOUN
while SCONJ
trying VERB
to PART
avoid VERB
the DET
political ADJ
minefields NOUN
of ADP
toxic ADJ
managers NOUN
and CCONJ
‘ PUNCT
yes INTJ
men NOUN
’ PUNCT
that PRON
wait VERB
us PRON
in ADP
every DET
corner NOUN
. PUNCT
SPACE
Turned VERB
a DET
blind ADJ
eye NOUN
to ADP
bully NOUN
engineers NOUN
turned VERB
managers NOUN
overnight ADV
and CCONJ
mastered VERB
avoiding VERB
sec PROPN
sociopaths NOUN
that PRON
were AUX
given VERB
management NOUN
duties NOUN
! PUNCT
SPACE
I PRON
had VERB
the DET
opportunity NOUN
to PART
be AUX
called VERB
into ADP
last ADJ
moment NOUN
meetings NOUN
about ADP
compliance NOUN
topics NOUN
that PRON
should AUX
have AUX
been AUX
done VERB
yesterday NOUN
, PUNCT
fudge VERB
the DET
way NOUN
to ADP
the DET
next ADJ
audit NOUN
to PART
avoid VERB
more ADJ
regulatory ADJ
fines NOUN
from ADP
Bafin PROPN
and CCONJ
hear VERB
about ADP
customer NOUN
data NOUN
stolen VERB
that SCONJ
nobody PRON
knows VERB
what PRON
they PRON
were AUX
! PUNCT
SPACE
The DET
company NOUN
mission NOUN
kept VERB
me PRON
on ADP
my PRON
feet NOUN
, PUNCT
year NOUN
after ADP
year NOUN
! PUNCT
Let VERB
’s PRON
do VERB
200 NUM
things NOUN
when SCONJ
realistically ADV
we PRON
can AUX
do VERB
50 NUM
. PUNCT
SPACE
Who PRON
cares VERB
as ADV
long ADV
as SCONJ
we PRON
can AUX
have VERB
cool ADJ
warehouse NOUN
parties NOUN
with ADP
the DET
founders NOUN
! PUNCT
SPACE
Total ADJ
lack NOUN
of ADP
focus NOUN
and CCONJ
working NOUN
processes NOUN
made VERB
me PRON
better ADJ
at ADP
useless ADJ
ping NOUN
- PUNCT
pong NOUN
with ADP
others NOUN
… PUNCT
almost ADV
championed VERB
the DET
public ADJ
sector NOUN
9 NUM
- SYM
3 NUM
style NOUN
of ADP
N26 PROPN
Bank PROPN
. PUNCT
SPACE
Unfortunately ADV
other ADJ
directors NOUN
that PRON
come VERB
and CCONJ
quit VERB
within ADP
the DET
year NOUN
were AUX
not PART
so ADV
determined ADJ
. PUNCT
So ADV
weak AUX
:( PUNCT
SPACE
Same PROPN
goes VERB
for ADP
all DET
those PRON
who PRON
disappeared VERB
on ADP
a DET
friday PROPN
afternoon NOUN
. PUNCT
You PRON
did AUX
n’t PART
see VERB
it PRON
coming VERB
, PUNCT
did VERB
you PRON
? PUNCT
SPACE
The DET
epitome NOUN
of ADP
success NOUN
is AUX
measured VERB
by ADP
how SCONJ
many ADJ
CTOs NOUN
we PRON
are AUX
changing VERB
within ADP
a DET
year NOUN
. PUNCT
Ask VERB
any DET
engineer NOUN
! PUNCT
SPACE
My PRON
favourite ADJ
interim ADJ
CTO PROPN
moment NOUN
was AUX
when SCONJ
our PRON
saviour NOUN
got AUX
promoted VERB
, PUNCT
fired VERB
managers NOUN
that PRON
disagreed VERB
with ADP
the DET
complete ADJ
lack NOUN
of ADP
plan NOUN
, PUNCT
toured VERB
around ADP
offices NOUN
like ADP
a DET
rockstar NOUN
with ADP
his PRON
baseball NOUN
cap NOUN
and CCONJ
‘ PUNCT
agile ADJ
consultant NOUN
’ PUNCT
supporters NOUN
, PUNCT
got AUX
demoted VERB
AND CCONJ
quit VERB
… PUNCT
ALL PRON
in ADP
6 NUM
months NOUN
. PUNCT
SPACE
What PRON
a DET
ride NOUN
! PUNCT
It PRON
got VERB
me PRON
more ADV
acquainted VERB
with ADP
kitkat PROPN
! PUNCT
SPACE
Finally ADV
, PUNCT
a DET
big ADJ
thanks NOUN
to ADP
the DET
HR PROPN
hard ADJ
core NOUN
. PUNCT
Ruthless ADJ
, PUNCT
trained VERB
in ADP
hire PROPN
& CCONJ
fire NOUN
techniques NOUN
, PUNCT
they PRON
always ADV
find VERB
time NOUN
if SCONJ
it PRON
’s VERB
about ADP
whipping VERB
. PUNCT
SPACE
With ADP
resourceful ADJ
denial NOUN
they PRON
have AUX
been AUX
downplaying VERB
the DET
countless ADJ
something PRON
- PUNCT
hit VERB
- PUNCT
the DET
- PUNCT
fan NOUN
moments NOUN
, PUNCT
giving VERB
us PRON
lots NOUN
of ADP
self NOUN
- PUNCT
praising VERB
bot NOUN
- PUNCT
style NOUN
answers NOUN
about ADP
N26 PROPN
achievements NOUN
! PUNCT
The DET
joy NOUN
! PUNCT
! PUNCT
SPACE
I PRON
love VERB
the DET
way NOUN
they PRON
take VERB
feedback NOUN
constantly ADV
over ADP
the DET
years NOUN
and CCONJ
making VERB
N26 PROPN
great ADJ
again ADV
! PUNCT
! PUNCT
SPACE
After ADV
all ADV
, PUNCT
it PRON
's AUX
no DET
surprise NOUN
that SCONJ
N26 PROPN
is AUX
the DET
top ADJ
startup NOUN
employer NOUN
in ADP
the DET
world NOUN
! PUNCT
Constantly ADV
hiring VERB
and CCONJ
in ADP
hypergrowth NOUN
. PUNCT
SPACE
For ADP
example NOUN
, PUNCT
since SCONJ
Feb PROPN
last ADJ
year NOUN
we PRON
are AUX
300 NUM
employees NOUN
less ADV
! PUNCT
If SCONJ
this PRON
is AUX
not PART
sustainable ADJ
growth NOUN
towards ADP
0 NUM
, PUNCT
what PRON
is AUX
it PRON
? PUNCT
! PUNCT
SPACE
Apollo PROPN
13 NUM
, PUNCT
is AUX
the DET
mission NOUN
that PRON
exactly ADV
matches VERB
N26 PROPN
. PUNCT
I PRON
’m VERB
proud ADJ
to PART
say VERB
that SCONJ
with ADP
such ADJ
determined ADJ
leadership NOUN
we PRON
made VERB
it PRON
already ADV
5 NUM
times NOUN
there ADV
! PUNCT
! PUNCT
Hurrah PROPN
hurrah INTJ
! PUNCT
Named Entity Recognition:
one day DATE
the last few years DATE
6-12 months DATE
-1 ORG
overnight TIME
sec ORG
yesterday DATE
Bafin LOC
year DATE
year DATE
200 CARDINAL
50 CARDINAL
9 CARDINAL
N26 Bank ORG
the year DATE
friday DATE
afternoon TIME
a year DATE
6 months DATE
the years DATE
N26 ORG
hypergrowth GPE
Feb last year DATE
300 CARDINAL
0 CARDINAL
Apollo 13 LAW
5 CARDINAL
Hurrah PERSON
--------------------------------------------------
Tokenization and POS Tagging:
Great ADJ
work NOUN
environment NOUN
, PUNCT
lots NOUN
of ADP
support NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Able ADJ
to PART
use VERB
a DET
wide ADJ
variety NOUN
of ADP
software NOUN
and CCONJ
tools NOUN
SPACE
- PUNCT
Able ADJ
to PART
be AUX
promoted VERB
within ADP
a DET
short ADJ
time NOUN
period NOUN
SPACE
- PUNCT
Beer NOUN
in ADP
the DET
kitchen NOUN
refrigerators NOUN
SPACE
- PUNCT
Flexible ADJ
schedule NOUN
and CCONJ
able ADJ
to PART
work VERB
remotely ADV
--------------------------------------------------
Tokenization and POS Tagging:
Equipment NOUN
, PUNCT
free ADJ
drinks NOUN
, PUNCT
super ADV
talented ADJ
colleagues NOUN
( PUNCT
up ADP
to ADP
TL PROPN
/ SYM
manager NOUN
level NOUN
only ADV
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
smart ADJ
people NOUN
from ADP
whom PRON
you PRON
can AUX
learn VERB
a DET
lot NOUN
. PUNCT
The DET
product NOUN
itself PRON
is AUX
really ADV
cool ADJ
and CCONJ
has VERB
a DET
lot NOUN
of ADP
potential NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Average ADJ
pay NOUN
for ADP
Berlin PROPN
SPACE
Nice PROPN
office NOUN
environment NOUN
Named Entity Recognition:
Berlin
Nice GPE
--------------------------------------------------
Tokenization and POS Tagging:
opportunities NOUN
to PART
make VERB
extra ADJ
for ADP
working NOUN
holidays NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Free ADJ
food NOUN
and CCONJ
drink NOUN
, PUNCT
cool ADJ
building NOUN
, PUNCT
nice ADJ
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Mostly ADV
nice ADJ
colleagues NOUN
SPACE
snacks NOUN
in ADP
the DET
office NOUN
SPACE
not PART
much ADJ
else ADV
--------------------------------------------------
Processing 'Cons':
Tokenization and POS Tagging:
Management NOUN
crisis NOUN
, PUNCT
high ADJ
turnover NOUN
on ADP
management NOUN
level NOUN
, PUNCT
from ADP
Leads NOUN
to ADP
C NOUN
- PUNCT
Level NOUN
Named Entity Recognition:
Leads to C-Level ORG
--------------------------------------------------
Tokenization and POS Tagging:
Management NOUN
is AUX
not PART
really ADV
ready ADJ
to PART
listen VERB
opinions NOUN
different ADJ
than ADP
theirs PRON
, PUNCT
and CCONJ
not PART
really ADV
concerned ADJ
about ADP
developing VERB
careers NOUN
. PUNCT
SPACE
Attrition NOUN
is AUX
high ADJ
. PUNCT
Salaries NOUN
are AUX
punished VERB
with ADP
the DET
flag NOUN
of ADP
" PUNCT
we PRON
have AUX
to PART
become VERB
profitable ADJ
" PUNCT
, PUNCT
but CCONJ
the DET
top ADJ
whole ADJ
compensation NOUN
schema PROPN
is AUX
blurred VERB
. PUNCT
SPACE
There PRON
is VERB
plenty NOUN
of ADP
evaluation NOUN
for ADP
Individual PROPN
Contributors PROPN
, PUNCT
but CCONJ
not PART
the DET
other ADJ
way NOUN
around ADV
. PUNCT
Despite SCONJ
there PRON
is VERB
a DET
supposedly ADV
anonymous ADJ
feedback NOUN
tool NOUN
, PUNCT
the DET
reports NOUN
are AUX
given VERB
by ADP
department NOUN
or CCONJ
team NOUN
, PUNCT
so CCONJ
if SCONJ
you PRON
are AUX
serious ADJ
answering NOUN
that PRON
is AUX
not PART
difficult ADJ
to PART
conclude VERB
who PRON
is AUX
writing VERB
. PUNCT
Named Entity Recognition:
Individual Contributors ORG
--------------------------------------------------
Tokenization and POS Tagging:
The DET
managers NOUN
in ADP
this DET
company NOUN
do AUX
not PART
support VERB
those DET
wonderful ADJ
people NOUN
, PUNCT
to ADP
the DET
contrary NOUN
, PUNCT
they PRON
create VERB
toxic ADJ
environments NOUN
and CCONJ
try VERB
to PART
pit VERB
coworkers NOUN
against ADP
each DET
other ADJ
, PUNCT
and CCONJ
also ADV
use VERB
lies NOUN
and CCONJ
bullying VERB
to PART
keep VERB
people NOUN
in ADP
line NOUN
or CCONJ
drive VERB
them PRON
away ADV
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
uncertainty NOUN
with ADP
organisation NOUN
direction NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Project NOUN
workflow NOUN
can AUX
be AUX
lost VERB
in ADP
the DET
thread NOUN
when SCONJ
changes NOUN
happen VERB
. PUNCT
It PRON
is AUX
easy ADJ
to PART
lose VERB
the DET
reason NOUN
why SCONJ
that DET
kind NOUN
of ADP
project NOUN
needs VERB
to PART
be AUX
delivered VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
so ADV
much ADJ
chance NOUN
of ADP
growth NOUN
--------------------------------------------------
Tokenization and POS Tagging:
the DET
salary NOUN
is AUX
quite ADV
low ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Too ADV
many ADJ
layers NOUN
in ADP
the DET
org NOUN
after ADP
scaling VERB
a DET
lot NOUN
very ADV
quickly ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
problems NOUN
with ADP
budget NOUN
regarding VERB
salary NOUN
updates NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Product NOUN
leaders NOUN
need VERB
more ADJ
focus NOUN
on ADP
being AUX
more ADJ
data NOUN
informed VERB
. PUNCT
The DET
data NOUN
insights VERB
culture NOUN
still ADV
needs VERB
some DET
maturing VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
-N26 PROPN
has AUX
had VERB
several ADJ
layoffs NOUN
, PUNCT
does AUX
n't PART
seem VERB
to PART
be AUX
a DET
company NOUN
where SCONJ
longevity NOUN
is AUX
possible ADJ
! PUNCT
! PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
terrible ADJ
management NOUN
, PUNCT
lots NOUN
of ADP
people NOUN
in ADP
my PRON
department NOUN
left VERB
already ADV
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
not PART
a DET
great ADJ
team NOUN
building NOUN
( PUNCT
noone NOUN
ever ADV
goes VERB
to ADP
the DET
office NOUN
) PUNCT
Named Entity Recognition:
noone TIME
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Not PART
a DET
lot NOUN
of ADP
freedom NOUN
--------------------------------------------------
Tokenization and POS Tagging:
not PART
organised ADJ
, PUNCT
not PART
transparent ADJ
, PUNCT
demanding VERB
, PUNCT
very ADV
very ADV
bad ADJ
paid VERB
--------------------------------------------------
Tokenization and POS Tagging:
Driving VERB
changes NOUN
sometimes ADV
take VERB
time NOUN
and CCONJ
not PART
every DET
team NOUN
member NOUN
understand VERB
around ADP
change NOUN
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
A DET
lot NOUN
of ADP
politics NOUN
between ADP
the DET
work PROPN
council NOUN
and CCONJ
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
it PRON
was AUX
a DET
bit NOUN
cowboy NOUN
like INTJ
, PUNCT
as ADP
in ADP
not PART
so ADV
many ADJ
mentorship NOUN
and CCONJ
support NOUN
systems NOUN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
only ADV
heard VERB
, PUNCT
but CCONJ
did AUX
n't PART
experience VERB
, PUNCT
that SCONJ
upper ADJ
management NOUN
can AUX
be AUX
quite ADV
toxic ADJ
and CCONJ
demanding VERB
SPACE
Also ADV
now ADV
, PUNCT
after ADP
a DET
couple NOUN
of ADP
rounds NOUN
of ADP
layoffs NOUN
, PUNCT
people NOUN
who PRON
stayed VERB
have VERB
much ADV
more ADJ
to PART
- PUNCT
dos NOUN
on ADP
their PRON
plates NOUN
and CCONJ
much ADV
more ADJ
pressure NOUN
--------------------------------------------------
Tokenization and POS Tagging:
There PRON
's VERB
a DET
lot NOUN
of ADP
technical ADJ
debt NOUN
, PUNCT
Compliance NOUN
requirements NOUN
and CCONJ
bureaucratic ADJ
overhead NOUN
can AUX
be AUX
significant ADJ
roadblocks NOUN
to ADP
getting VERB
things NOUN
done VERB
. PUNCT
SPACE
Company NOUN
values NOUN
for ADP
diversity NOUN
and CCONJ
inclusion NOUN
are AUX
very ADV
progressive ADJ
, PUNCT
but CCONJ
this PRON
is AUX
not PART
reflected VERB
in ADP
upper ADJ
management NOUN
. PUNCT
SPACE
Company NOUN
- PUNCT
wide ADJ
initiatives NOUN
and CCONJ
changes NOUN
in ADP
direction NOUN
can AUX
be AUX
quite ADV
disruptive ADJ
to ADP
accomplishing VERB
goals NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
lack NOUN
of ADP
stable ADJ
leadership NOUN
, PUNCT
high ADJ
attrition NOUN
rates NOUN
, PUNCT
dissatisfied ADJ
employees NOUN
, PUNCT
toxic ADJ
work NOUN
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
No DET
strong ADJ
career NOUN
prospects NOUN
and CCONJ
weak ADJ
compensation NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Everyone PRON
is AUX
busy ADJ
and CCONJ
it PRON
can AUX
be AUX
hard ADJ
as SCONJ
a DET
manager NOUN
to PART
get VERB
support NOUN
from ADP
others NOUN
or CCONJ
even ADV
to PART
work VERB
on ADP
projects NOUN
in ADP
collaboration NOUN
with ADP
other ADJ
managers NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
at ADP
the DET
moment NOUN
to PART
share VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
a DET
bit NOUN
toxic ADJ
at ADP
times NOUN
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
do AUX
n't PART
have VERB
obvious ADJ
cons NOUN
to PART
report VERB
. PUNCT
The DET
environment NOUN
was AUX
fast ADV
paced VERB
, PUNCT
which PRON
I PRON
loved VERB
, PUNCT
the DET
consequence NOUN
of ADP
that PRON
is AUX
that SCONJ
the DET
culture NOUN
aims VERB
to PART
innovate VERB
and CCONJ
is AUX
willing ADJ
to PART
fail/ VERB
sacrifice NOUN
efficiency NOUN
however ADV
, PUNCT
which PRON
made VERB
the DET
experience NOUN
very ADV
energizing VERB
for ADP
me PRON
. PUNCT
I PRON
can AUX
see VERB
how SCONJ
it PRON
might AUX
be AUX
taxing VERB
for ADP
others NOUN
however ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Company NOUN
culture NOUN
not PART
aligned VERB
with ADP
what PRON
they PRON
assess VERB
--------------------------------------------------
Tokenization and POS Tagging:
The DET
workforce NOUN
planning NOUN
was AUX
erratic ADJ
, PUNCT
with ADP
frequent ADJ
shifts NOUN
in ADP
outlook NOUN
, PUNCT
leading VERB
to ADP
an DET
environment NOUN
of ADP
perpetual ADJ
uncertainty NOUN
. PUNCT
Transparency NOUN
was AUX
not PART
just ADV
lacking VERB
but CCONJ
replaced VERB
by ADP
a DET
disconcerting VERB
pattern NOUN
of ADP
deceit NOUN
, PUNCT
eroding VERB
trust NOUN
within ADP
the DET
team NOUN
. PUNCT
SPACE
A DET
particularly ADV
disheartening ADJ
episode NOUN
was AUX
the DET
handling NOUN
of ADP
mass NOUN
layoffs NOUN
, PUNCT
with ADP
the DET
Talent PROPN
Acquisition PROPN
team NOUN
facing VERB
layoffs NOUN
three NUM
times NOUN
within ADP
18 NUM
months NOUN
. PUNCT
Dubious ADJ
reasons NOUN
coerced VERB
team NOUN
members NOUN
into ADP
signing VERB
mutual ADJ
termination NOUN
agreements NOUN
, PUNCT
only ADV
for SCONJ
the DET
company NOUN
to PART
later ADV
claim VERB
imminent ADJ
profitability NOUN
. PUNCT
SPACE
TA PROPN
team NOUN
's PART
work NOUN
culture NOUN
is AUX
tainted VERB
by ADP
favoritism PROPN
, PUNCT
where SCONJ
climbing VERB
the DET
career NOUN
ladder NOUN
is AUX
more ADJ
about ADP
being AUX
a DET
' PUNCT
yes NOUN
- PUNCT
person NOUN
' PUNCT
than ADP
actual ADJ
competence NOUN
. PUNCT
Those PRON
willing ADJ
to PART
act VERB
as ADP
unquestioning VERB
puppets NOUN
are AUX
favored VERB
, PUNCT
irrespective ADV
of ADP
their PRON
contributions NOUN
. PUNCT
SPACE
For ADP
those PRON
seeking VERB
a DET
workplace NOUN
that PRON
values VERB
genuine ADJ
effort NOUN
and CCONJ
encourages VERB
career NOUN
growth NOUN
based VERB
on ADP
merit NOUN
, PUNCT
please INTJ
think VERB
twice ADV
. PUNCT
The DET
constant ADJ
threat NOUN
of ADP
layoffs NOUN
and CCONJ
a DET
culture NOUN
of ADP
fear NOUN
make VERB
professional ADJ
development NOUN
secondary ADJ
to ADP
survival NOUN
. PUNCT
Named Entity Recognition:
Talent Acquisition ORG
three CARDINAL
18 months DATE
--------------------------------------------------
Tokenization and POS Tagging:
no DET
meritocracy NOUN
take VERB
into ADP
consideration NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Terrible ADJ
team NOUN
culture NOUN
, PUNCT
managers NOUN
are AUX
not PART
trained VERB
on ADP
people NOUN
management NOUN
, PUNCT
higher ADJ
ups NOUN
are AUX
not PART
motivated VERB
to PART
work VERB
they PRON
are AUX
just ADV
sitting VERB
on ADP
their PRON
cushy ADJ
salaries NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Heavy ADJ
workflow NOUN
. PUNCT
Late ADJ
shift NOUN
rotation NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Started VERB
2 NUM
years NOUN
ago ADV
and CCONJ
everything PRON
was AUX
perfect ADJ
, PUNCT
growth NOUN
opportunities NOUN
, PUNCT
commitment NOUN
from ADP
the DET
management NOUN
towards ADP
benefit NOUN
maximization NOUN
, PUNCT
empathy NOUN
and CCONJ
a DET
great ADJ
communication NOUN
flow NOUN
. PUNCT
In ADP
the DET
past ADJ
1 NUM
year NOUN
at ADP
least ADJ
it PRON
seems VERB
like ADP
a DET
totally ADV
different ADJ
company NOUN
; PUNCT
all DET
those DET
great ADJ
values NOUN
are AUX
now ADV
missing VERB
, PUNCT
there PRON
is VERB
no DET
clear ADJ
path NOUN
towards ADP
growth NOUN
, PUNCT
promotions NOUN
and CCONJ
compensation NOUN
budgets NOUN
are AUX
minimal ADJ
( PUNCT
and CCONJ
on ADP
hold NOUN
) PUNCT
and CCONJ
the DET
political ADJ
environment NOUN
between ADP
management NOUN
and CCONJ
the DET
WoCo PROPN
is AUX
worse ADJ
than ADP
ever ADV
. PUNCT
- PUNCT
Management NOUN
lacks VERB
open ADJ
communication NOUN
and CCONJ
turnover NOUN
is AUX
skyrocketing VERB
, PUNCT
sadly ADV
with ADP
the DET
most ADV
capable ADJ
colleagues NOUN
, PUNCT
not PART
to PART
say VERB
those PRON
that PRON
were AUX
offered VERB
to PART
leave VERB
the DET
company NOUN
due ADP
to ADP
' PUNCT
strategic ADJ
priorities NOUN
' PART
Named Entity Recognition:
2 years ago DATE
the past 1 year DATE
WoCo ORG
--------------------------------------------------
Tokenization and POS Tagging:
Do AUX
they PRON
really ADV
care VERB
about ADP
you PRON
, PUNCT
or CCONJ
are AUX
you PRON
just ADV
another DET
TA NOUN
number NOUN
... PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Leadership PROPN
Performance PROPN
reviews VERB
Salaries NOUN
Benefits NOUN
--------------------------------------------------
Tokenization and POS Tagging:
company NOUN
with ADP
terrible ADJ
management NOUN
and CCONJ
issues NOUN
of ADP
communication NOUN
, PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
At ADP
the DET
moment NOUN
, PUNCT
company NOUN
is AUX
trying VERB
to PART
find VERB
its PRON
pace NOUN
again ADV
, PUNCT
which PRON
is AUX
great ADJ
but CCONJ
demand VERB
a DET
lot NOUN
of ADP
work NOUN
and CCONJ
commitment NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Top PROPN
Management PROPN
Politics PROPN
Works PROPN
Council PROPN
Named Entity Recognition:
Top Management Politics Works Council ORG
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
many ADJ
but CCONJ
the DET
financial ADJ
environment NOUN
is AUX
not PART
benefiting VERB
now ADV
tech NOUN
companies NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
political ADJ
environment NOUN
across ADP
most ADJ
departments NOUN
, PUNCT
the DET
people NOUN
who PRON
get AUX
promoted VERB
are AUX
the DET
not PART
the DET
ones NOUN
who PRON
necessarily ADV
deserve VERB
it PRON
, PUNCT
very ADV
uninspiring ADJ
, PUNCT
sometimes ADV
incompetent ADJ
and CCONJ
often ADV
inexperienced ADJ
leaders NOUN
. PUNCT
Employees NOUN
known VERB
it PRON
, PUNCT
but CCONJ
do AUX
n’t PART
bother VERB
to PART
say VERB
it PRON
, PUNCT
as SCONJ
the DET
feedback NOUN
culture NOUN
does AUX
n’t PART
work VERB
the DET
same ADJ
for ADP
everyone PRON
. PUNCT
Benefits NOUN
are AUX
not PART
competitive ADJ
at ADV
all ADV
and CCONJ
no PRON
budgets NOUN
to PART
work VERB
with ADP
, PUNCT
just ADV
big ADJ
unrealistic ADJ
expectations NOUN
. PUNCT
Great ADJ
employees NOUN
are AUX
leaving VERB
in ADP
big ADJ
numbers NOUN
nowadays ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Converging VERB
towards ADP
a DET
hierarchical ADJ
approach NOUN
in ADP
decision NOUN
- PUNCT
making NOUN
and CCONJ
problem NOUN
- PUNCT
solving NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Sometimes ADV
lack NOUN
of ADP
domain NOUN
expertise NOUN
due ADP
to ADP
average ADJ
age NOUN
of ADP
staff NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
company NOUN
should AUX
introduce VERB
bonuses NOUN
for ADP
employees NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Poor ADJ
growth NOUN
- PUNCT
Poor PROPN
Compensation NOUN
- PUNCT
Lack NOUN
of ADP
communication NOUN
- PUNCT
Lack NOUN
of ADP
transparency NOUN
- PUNCT
Chaotic ADJ
--------------------------------------------------
Tokenization and POS Tagging:
it PRON
is AUX
not PART
always ADV
as SCONJ
they PRON
say VERB
it PRON
is AUX
gon VERB
na PART
be AUX
--------------------------------------------------
Tokenization and POS Tagging:
1 X
. PUNCT
Limited PROPN
Internal PROPN
Mobility PROPN
Options PROPN
2 NUM
. PUNCT
No DET
Performance PROPN
Bonus PROPN
system NOUN
. PUNCT
Named Entity Recognition:
1 CARDINAL
Limited Internal Mobility Options ORG
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
all DET
departments NOUN
are AUX
treated VERB
equally ADV
--------------------------------------------------
Tokenization and POS Tagging:
Multiple ADJ
stakeholders NOUN
involved VERB
in ADP
projects NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Avg NOUN
. PUNCT
salary NOUN
- PUNCT
Stressful ADJ
--------------------------------------------------
Tokenization and POS Tagging:
BaFin PROPN
regulations NOUN
lead VERB
to ADP
some DET
roadblocks NOUN
to ADP
every DET
day NOUN
tasks VERB
Named Entity Recognition:
BaFin LOC
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
had VERB
a DET
feeling NOUN
that SCONJ
management NOUN
did AUX
n't PART
know VERB
how SCONJ
to PART
run VERB
business NOUN
/ SYM
work NOUN
and CCONJ
their PRON
poor ADJ
decisions NOUN
/ SYM
actions NOUN
can AUX
take VERB
a DET
company NOUN
down ADP
. PUNCT
The DET
hiring VERB
strategy NOUN
is AUX
also ADV
weird ADJ
- PUNCT
hiring VERB
foreigners NOUN
from ADP
abroad ADV
as SCONJ
they PRON
are AUX
just ADV
cheap ADJ
labor NOUN
cost NOUN
but CCONJ
very ADV
often ADV
are AUX
have VERB
bad ADJ
qualifications NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
A DET
lot NOUN
of ADP
processes NOUN
going VERB
on ADP
at ADP
once ADV
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
do AUX
n't PART
have VERB
anything PRON
right ADV
now ADV
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
high ADJ
employee NOUN
turnover NOUN
, PUNCT
hiring VERB
freeze NOUN
, PUNCT
salary NOUN
freeze NOUN
, PUNCT
recent ADJ
layoffs NOUN
, PUNCT
not PART
supporting VERB
management NOUN
, PUNCT
very ADV
unstable ADJ
and CCONJ
oftentimes ADJ
toxic ADJ
working VERB
environment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Sidelined VERB
for ADP
positions NOUN
that PRON
were AUX
already ADV
going VERB
to PART
be AUX
filled VERB
by ADP
particular ADJ
person NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Lacking ADJ
opportunities NOUN
for ADP
promotions NOUN
and CCONJ
salary NOUN
review NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Still ADV
learning VERB
how SCONJ
to PART
transition VERB
to ADP
a DET
sizable ADJ
complany NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Serious ADJ
lack NOUN
of ADP
overall ADJ
organisation NOUN
at ADP
top ADJ
management NOUN
level NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Lots NOUN
of ADP
structural ADJ
changes NOUN
can AUX
happen VERB
in ADP
short ADJ
time NOUN
, PUNCT
which PRON
makes VERB
it PRON
difficult ADJ
to PART
have VERB
an DET
established VERB
set NOUN
of ADP
goals NOUN
for ADP
each DET
position NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Long ADV
working VERB
hours NOUN
, PUNCT
unclear ADJ
strategy NOUN
and CCONJ
weak ADJ
human ADJ
- PUNCT
centric ADJ
leadership NOUN
Named Entity Recognition:
Long working hours TIME
--------------------------------------------------
Tokenization and POS Tagging:
C NOUN
- PUNCT
level NOUN
and CCONJ
the DET
Management PROPN
do AUX
not PART
care VERB
about ADP
employees NOUN
and CCONJ
are AUX
very ADV
chaotic ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Leadership NOUN
not PART
open ADJ
to PART
feedback VERB
/ SYM
critical ADJ
about ADP
the DET
own ADJ
performance NOUN
--------------------------------------------------
Tokenization and POS Tagging:
You PRON
get VERB
out ADP
what PRON
you PRON
put VERB
in ADP
, PUNCT
but CCONJ
tight ADJ
deadlines NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Founders NOUN
( PUNCT
who PRON
still ADV
hold VERB
c NOUN
- PUNCT
level NOUN
positions NOUN
) PUNCT
clash VERB
constantly ADV
with ADP
employees NOUN
in ADP
many ADJ
fronts NOUN
- PUNCT
Silos NOUN
everywhere ADV
by ADP
design NOUN
with ADP
eventual ADJ
collaboration NOUN
efforts NOUN
for ADP
specific ADJ
cases NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Informal ADJ
organization NOUN
with ADP
conflicting ADJ
centers NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Terrible ADJ
management NOUN
. PUNCT
Constant ADJ
change NOUN
of ADP
teams NOUN
and CCONJ
structure NOUN
therefore ADV
your PRON
job NOUN
is AUX
not PART
secure ADJ
. PUNCT
There PRON
is VERB
official ADJ
and CCONJ
unofficial ADJ
layoffs NOUN
all DET
the DET
time NOUN
. PUNCT
Zero NUM
transparency NOUN
over ADP
how SCONJ
the DET
company NOUN
is AUX
doing VERB
. PUNCT
you PRON
just ADV
wo AUX
n’t PART
know VERB
if SCONJ
they PRON
’re AUX
going VERB
bankrupt ADJ
or CCONJ
ipo PROPN
. PUNCT
Employees NOUN
worth VERB
nothing PRON
to ADP
upper ADJ
management NOUN
. PUNCT
you PRON
stay VERB
you PRON
leave VERB
or CCONJ
you PRON
die VERB
, PUNCT
no DET
one NOUN
cares VERB
. PUNCT
Promotions NOUN
happen VERB
only ADV
to ADP
specific ADJ
people NOUN
. PUNCT
Named Entity Recognition:
Zero CARDINAL
ipo ORG
--------------------------------------------------
Tokenization and POS Tagging:
Senior ADJ
leadership NOUN
seems VERB
to PART
be AUX
living VERB
in ADP
a DET
parallel ADJ
world NOUN
where SCONJ
they PRON
know VERB
everything PRON
and CCONJ
levels NOUN
below ADV
are AUX
there PRON
just ADV
to PART
execute VERB
their PRON
horribly ADV
planned VERB
ideas NOUN
. PUNCT
Oh INTJ
, PUNCT
and CCONJ
diversity NOUN
... PUNCT
well INTJ
, PUNCT
that PRON
's AUX
a DET
whole ADJ
topic NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
not PART
clear ADJ
growth NOUN
plan NOUN
burning VERB
deadlines NOUN
every DET
time NOUN
Top ADJ
management NOUN
Politics NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Terrible ADJ
management NOUN
, PUNCT
zero NUM
transparency NOUN
, PUNCT
employees NOUN
do AUX
not PART
have VERB
value NOUN
they PRON
're AUX
just ADV
numbers NOUN
, PUNCT
constant ADJ
team NOUN
changes NOUN
, PUNCT
company NOUN
wide ADJ
policies NOUN
change NOUN
by ADP
day NOUN
, PUNCT
start NOUN
of ADP
month NOUN
company NOUN
could AUX
be AUX
a DET
remote NOUN
- PUNCT
only ADV
, PUNCT
end NOUN
of ADP
month NOUN
it PRON
could AUX
become VERB
office NOUN
only ADV
. PUNCT
Or CCONJ
one NUM
day NOUN
it PRON
's AUX
all ADV
good ADJ
, PUNCT
the DET
other ADJ
day NOUN
they PRON
have VERB
to PART
layoff VERB
people NOUN
randomly ADV
. PUNCT
Named Entity Recognition:
zero CARDINAL
day DATE
month DATE
one day DATE
the other day DATE
--------------------------------------------------
Tokenization and POS Tagging:
Bad PROPN
Management PROPN
Many ADJ
tasks NOUN
to PART
do VERB
Micromanagement NOUN
Named Entity Recognition:
Bad Management ORG
--------------------------------------------------
Tokenization and POS Tagging:
Where SCONJ
to PART
start VERB
and CCONJ
when SCONJ
to PART
stop VERB
... PUNCT
? PUNCT
SPACE
Appalling VERB
behaviour NOUN
towards ADP
the DET
workforce NOUN
at ADP
top ADJ
management NOUN
level NOUN
, PUNCT
reflected VERB
in ADP
the DET
numerous ADJ
open ADJ
court NOUN
conflicts NOUN
between ADP
employer NOUN
and CCONJ
employees NOUN
' PART
representation NOUN
. PUNCT
Recent ADJ
media NOUN
leaks NOUN
confirm VERB
reprehensible ADJ
behaviour NOUN
even ADV
among ADP
themselves PRON
. PUNCT
SPACE
" PUNCT
Cost NOUN
cutting NOUN
" PUNCT
measures NOUN
that PRON
border NOUN
on ADP
the DET
ridiculous ADJ
and CCONJ
are AUX
actively ADV
hindering VERB
productivity NOUN
& CCONJ
development PROPN
, PUNCT
the DET
type NOUN
of ADP
" PUNCT
shoot VERB
oneself PRON
in ADP
the DET
foot NOUN
" PUNCT
measures NOUN
. PUNCT
SPACE
Unclear ADJ
career NOUN
paths NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
do AUX
n’t PART
have VERB
anything PRON
to PART
add VERB
here ADV
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Hard ADJ
to PART
get VERB
promotion NOUN
--------------------------------------------------
Tokenization and POS Tagging:
One NUM
area NOUN
where SCONJ
N26 PROPN
could AUX
improve VERB
is AUX
compensation NOUN
. PUNCT
Salaries NOUN
and CCONJ
compensation NOUN
are AUX
lower ADJ
than ADP
the DET
market NOUN
average NOUN
, PUNCT
which PRON
can AUX
be AUX
frustrating ADJ
at ADP
times NOUN
. PUNCT
Also ADV
, PUNCT
due ADP
to ADP
the DET
highly ADV
regulated ADJ
nature NOUN
of ADP
the DET
business NOUN
, PUNCT
programs NOUN
can AUX
move VERB
slower ADV
than ADP
other ADJ
domains NOUN
unless SCONJ
you PRON
have VERB
prior ADJ
experience NOUN
in ADP
a DET
similar ADJ
environment NOUN
. PUNCT
Named Entity Recognition:
One CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Job PROPN
security NOUN
is AUX
not PART
always ADV
a DET
certainty NOUN
given VERB
N26 PROPN
's PART
poor ADJ
record NOUN
of ADP
compliance NOUN
and CCONJ
reputation NOUN
as ADP
an DET
organisation NOUN
; PUNCT
also ADV
management NOUN
is AUX
very ADV
quick ADJ
to PART
fire VERB
people NOUN
Named Entity Recognition:
N26 ORG
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
sometimes ADV
top VERB
down ADP
decisions NOUN
/ SYM
plannings NOUN
--------------------------------------------------
Tokenization and POS Tagging:
poor ADJ
management NOUN
and CCONJ
leadership NOUN
, PUNCT
no DET
focus NOUN
on ADP
retention NOUN
and CCONJ
internal ADJ
career NOUN
development NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
It PRON
's AUX
a DET
bank NOUN
. PUNCT
They PRON
will AUX
sell VERB
themselves PRON
as ADP
an DET
amazing ADJ
tech NOUN
company NOUN
, PUNCT
but CCONJ
inside ADV
it PRON
is AUX
a DET
bank NOUN
. PUNCT
- PUNCT
The DET
organization NOUN
is AUX
designed VERB
without ADP
proper ADJ
incentives NOUN
and CCONJ
it PRON
is AUX
highly ADV
disfunctional ADJ
. PUNCT
- PUNCT
High ADJ
turnover NOUN
rate NOUN
of ADP
top ADJ
management NOUN
and CCONJ
employees NOUN
. PUNCT
No DET
proper ADJ
policies NOUN
for ADP
retaining VERB
the DET
talents NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Culture NOUN
is AUX
not PART
people NOUN
oriented ADJ
, PUNCT
leadership NOUN
expecta VERB
high ADJ
rotation NOUN
and CCONJ
investment NOUN
in ADP
retention NOUN
is AUX
low ADJ
--------------------------------------------------
Tokenization and POS Tagging:
None NOUN
for ADP
a DET
big ADJ
startup NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
working VERB
culture NOUN
- PUNCT
salary NOUN
- PUNCT
leadership NOUN
team NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
low ADJ
pay NOUN
and CCONJ
layoffs NOUN
when SCONJ
market NOUN
closed VERB
--------------------------------------------------
Tokenization and POS Tagging:
White ADJ
boys NOUN
club NOUN
story NOUN
is AUX
true ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Solving VERB
new ADJ
issues NOUN
/ SYM
problems NOUN
on ADP
a DET
daily ADJ
basis NOUN
Named Entity Recognition:
daily DATE
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
do AUX
n't PART
really ADV
have VERB
anything PRON
bad ADJ
to PART
say VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Many ADJ
tasks NOUN
to PART
do VERB
, PUNCT
working VERB
constantly ADV
under ADP
pressure NOUN
. PUNCT
Management NOUN
without ADP
soft ADJ
skills NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
chaos NOUN
and CCONJ
no DET
clear ADJ
vision NOUN
. PUNCT
- PUNCT
managers NOUN
have VERB
no DET
idea NOUN
how SCONJ
to PART
manage VERB
ICs NOUN
, PUNCT
whereas SCONJ
agile ADJ
coaches NOUN
are AUX
leading VERB
the DET
company NOUN
. PUNCT
- PUNCT
Average ADJ
compensations NOUN
. PUNCT
- PUNCT
no DET
clear ADJ
guides NOUN
on ADP
how SCONJ
to PART
get AUX
promoted VERB
--------------------------------------------------
Tokenization and POS Tagging:
None NOUN
that PRON
come VERB
to PART
mind VERB
--------------------------------------------------
Tokenization and POS Tagging:
Senior ADJ
management NOUN
was AUX
a DET
bit NOUN
shaky ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
A PROPN
's PART
hire VERB
A PROPN
's PART
, PUNCT
B PROPN
's PART
hire NOUN
C PROPN
's PART
. PUNCT
As SCONJ
the DET
company NOUN
went VERB
through ADP
hypergrowth NOUN
the DET
quality NOUN
of ADP
new ADJ
team NOUN
members NOUN
went VERB
off ADP
a DET
cliff NOUN
. PUNCT
Became VERB
commonplace ADJ
for SCONJ
a DET
meeting NOUN
to PART
have VERB
10 NUM
+ SYM
people NOUN
, PUNCT
none NOUN
of ADP
which PRON
could AUX
actually ADV
do VERB
anything PRON
or CCONJ
had VERB
decision NOUN
power NOUN
- PUNCT
Extremely ADV
volatile ADJ
c ADJ
- PUNCT
level NOUN
management NOUN
. PUNCT
Poor ADJ
strategic ADJ
product NOUN
understanding NOUN
Named Entity Recognition:
B ORG
C ORG
10 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Salaries NOUN
used VERB
to PART
be AUX
a DET
bit NOUN
low ADJ
and CCONJ
it PRON
's AUX
a DET
founder NOUN
led VERB
company NOUN
implying VERB
that SCONJ
loads NOUN
of ADP
decisions NOUN
are AUX
taken VERB
by ADP
founders NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Terrible ADJ
management NOUN
and CCONJ
culture NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Unclear ADJ
personal ADJ
growth NOUN
path NOUN
Bonuses NOUN
and CCONJ
stocks NOUN
not PART
available ADJ
under ADP
senior ADJ
levels NOUN
* PUNCT
* PUNCT
under ADP
change NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Sometimes ADV
unstructured ADJ
communication NOUN
und ADV
collab VERB
in ADP
between ADP
teams NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Silos NOUN
communication NOUN
between ADP
department NOUN
, PUNCT
fast ADJ
pace NOUN
( PUNCT
could AUX
also ADV
be AUX
a DET
pro NOUN
I PRON
guess VERB
) PUNCT
, PUNCT
turn VERB
over ADP
in ADP
higher ADJ
management NOUN
. PUNCT
Good ADJ
culture NOUN
and CCONJ
values NOUN
--------------------------------------------------
Tokenization and POS Tagging:
little ADJ
work NOUN
life NOUN
balance NOUN
, PUNCT
little ADJ
flexibility NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
great ADJ
with ADP
remote ADJ
work NOUN
Work NOUN
laptops NOUN
are AUX
all PRON
very ADV
limited ADJ
No DET
bonuses NOUN
Does AUX
nt PART
offer VERB
any DET
mental ADJ
health NOUN
support NOUN
, PUNCT
or CCONJ
any DET
incentives NOUN
to PART
stay VERB
( PUNCT
in ADP
comparison NOUN
to ADP
other ADJ
companies NOUN
) PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
not PART
enough ADJ
benefits NOUN
pay NOUN
could AUX
be AUX
higher ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Thieves NOUN
because SCONJ
they PRON
're AUX
not PART
subject ADJ
to ADP
the DET
central ADJ
bank NOUN
which PRON
is AUX
governed VERB
via ADP
a DET
country NOUN
rules NOUN
--------------------------------------------------
Tokenization and POS Tagging:
work NOUN
is AUX
repetitive ADJ
and CCONJ
not PART
that PRON
motivating VERB
--------------------------------------------------
Tokenization and POS Tagging:
One PRON
has VERB
to PART
asked VERB
to PART
be AUX
mentored VERB
, PUNCT
sometimes ADV
a DET
little ADJ
bit NOUN
unstructured ADJ
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
applicable ADJ
as ADP
of ADP
now ADV
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
everything PRON
is AUX
so ADV
well ADV
planned VERB
--------------------------------------------------
Tokenization and POS Tagging:
growing VERB
enviroment NOUN
, PUNCT
therefore ADV
no DET
perfect ADJ
structure NOUN
spontaneous ADJ
tasks NOUN
, PUNCT
which PRON
can AUX
take VERB
long ADV
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
have AUX
been AUX
working VERB
for ADP
N26 PROPN
for ADP
almost ADV
9 NUM
months NOUN
and CCONJ
I PRON
could AUX
n't PART
be AUX
happier ADJ
. PUNCT
I PRON
do AUX
not PART
have VERB
any DET
" PUNCT
con NOUN
" PUNCT
to PART
mention VERB
here ADV
. PUNCT
Named Entity Recognition:
almost 9 months DATE
--------------------------------------------------
Tokenization and POS Tagging:
C PROPN
Level PROPN
could AUX
do VERB
better ADV
into ADP
earning VERB
employees NOUN
trusts NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
I PRON
do AUX
not PART
remember VERB
any DET
kontras NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Leadership NOUN
, PUNCT
messy ADJ
priorities NOUN
and CCONJ
processes NOUN
to PART
be AUX
defined VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Managed VERB
terribly ADV
, PUNCT
serial ADJ
quitting NOUN
, PUNCT
impossible ADJ
to PART
get VERB
a DET
permanent ADJ
contract NOUN
in ADP
CS PROPN
. PUNCT
The DET
product NOUN
was AUX
riddled VERB
with ADP
issues NOUN
in ADP
2020 NUM
that PRON
ca AUX
n't PART
have AUX
been AUX
solved VERB
by ADP
now ADV
. PUNCT
Named Entity Recognition:
CS GPE
2020 DATE
--------------------------------------------------
Tokenization and POS Tagging:
You PRON
do AUX
n't PART
meet VERB
a DET
lot NOUN
of ADP
external ADJ
departments NOUN
in ADP
addition NOUN
to ADP
yours PRON
because SCONJ
N26 PROPN
is AUX
made VERB
out ADP
of ADP
more ADJ
than ADP
800 NUM
people NOUN
. PUNCT
Named Entity Recognition:
more than 800 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Slow ADJ
process NOUN
to PART
do AUX
most ADJ
of ADP
the DET
things NOUN
SPACE
- PUNCT
Leadership NOUN
can AUX
change VERB
their PRON
decisions NOUN
quite ADV
fast ADV
sometimes ADV
--------------------------------------------------
Tokenization and POS Tagging:
1 X
. PUNCT
Middle ADJ
management NOUN
/ SYM
heads NOUN
of ADP
etc X
. X
are AUX
really ADV
problematic ADJ
. PUNCT
They PRON
are AUX
n’t PART
competent ADJ
or CCONJ
are AUX
simply ADV
lazy ADJ
. PUNCT
You PRON
’ll AUX
completely ADV
lift VERB
and CCONJ
drive VERB
an DET
entire ADJ
project NOUN
and CCONJ
they PRON
’ll AUX
still ADV
somehow ADV
try VERB
and CCONJ
steal VERB
credit NOUN
for ADP
it PRON
which PRON
I PRON
find VERB
very ADV
morally ADV
problematic ADJ
. PUNCT
Additionally ADV
, PUNCT
they PRON
’re VERB
rude ADJ
and CCONJ
entitled VERB
. PUNCT
This PRON
is AUX
known VERB
by ADP
the DET
whole ADJ
department NOUN
though ADV
. PUNCT
They PRON
need VERB
a DET
huge ADJ
overhaul NOUN
of ADP
these DET
types NOUN
of ADP
people NOUN
and CCONJ
really ADV
get VERB
strong ADJ
leaders NOUN
with ADP
extensive ADJ
experience NOUN
in ADP
their PRON
subject NOUN
matter NOUN
. PUNCT
A DET
little ADJ
emotional ADJ
intelligence NOUN
would AUX
n’t PART
hurt VERB
either ADV
. PUNCT
They PRON
’ve AUX
been AUX
here ADV
long ADV
enough ADV
, PUNCT
time NOUN
to PART
go VERB
. PUNCT
SPACE
2 X
. PUNCT
The DET
workload NOUN
can AUX
be AUX
stressful ADJ
, PUNCT
work NOUN
life NOUN
balance NOUN
is AUX
not PART
a DET
value NOUN
touted VERB
in ADP
my PRON
department NOUN
at ADV
all ADV
. PUNCT
SPACE
3 X
. PUNCT
Promotion NOUN
structure NOUN
is AUX
very ADV
messy ADJ
. PUNCT
Woco PROPN
has AUX
been AUX
trying VERB
to PART
change VERB
this PRON
I PRON
think VERB
, PUNCT
but CCONJ
I PRON
think VERB
promotions NOUN
are AUX
n’t PART
led VERB
by ADP
a DET
very ADV
organized ADJ
team NOUN
. PUNCT
A DET
lot NOUN
of ADP
information NOUN
around ADP
this PRON
is AUX
outdated VERB
on ADP
their PRON
internal ADJ
sites NOUN
and CCONJ
their PRON
solution NOUN
to ADP
this PRON
is AUX
2 NUM
weeks NOUN
before ADP
a DET
cycle NOUN
to PART
have VERB
big ADJ
info NOUN
sessions NOUN
. PUNCT
People NOUN
care VERB
a DET
lot NOUN
about ADP
promotions NOUN
, PUNCT
the DET
people NOUN
team NOUN
should AUX
at ADP
least ADJ
act VERB
they PRON
do VERB
too ADV
. PUNCT
Named Entity Recognition:
1 CARDINAL
2 CARDINAL
3 CARDINAL
2 weeks DATE
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Management PROPN
leaves VERB
a DET
lot NOUN
to PART
be AUX
desired VERB
for ADP
SPACE
- PUNCT
Backstabbery PROPN
SPACE
- PUNCT
Middle ADJ
/ SYM
upper ADJ
management NOUN
engage VERB
in ADP
petty ADJ
politics NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Like ADP
all DET
other ADJ
companies NOUN
in ADP
the DET
planet NOUN
, PUNCT
there PRON
is VERB
always ADV
issues NOUN
. PUNCT
In ADP
a DET
regulated ADJ
bank NOUN
, PUNCT
expect VERB
to PART
have VERB
to PART
do VERB
boring ADJ
stuff NOUN
a DET
lot NOUN
. PUNCT
But CCONJ
the DET
balance NOUN
is AUX
acceptable ADJ
. PUNCT
But CCONJ
do AUX
n’t PART
expect VERB
it PRON
to PART
be AUX
a DET
startup NOUN
. PUNCT
It PRON
is AUX
past ADP
that DET
stage NOUN
for ADP
ages NOUN
. PUNCT
It PRON
’s VERB
a DET
big ADJ
co. NOUN
Now INTJ
so ADV
you PRON
do AUX
know VERB
what PRON
you PRON
are AUX
signing VERB
up ADP
for ADP
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
sometimes ADV
toxic ADJ
culture NOUN
, PUNCT
especially ADV
management NOUN
and CCONJ
founders NOUN
do AUX
n't PART
even ADV
say VERB
hello PROPN
--------------------------------------------------
Tokenization and POS Tagging:
Maybe ADV
sometimes ADV
it PRON
is AUX
hard ADJ
to PART
find VERB
other ADJ
positions NOUN
in ADP
other ADJ
departments NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
unstructured ADJ
processes NOUN
SPACE
- PUNCT
high ADJ
employee NOUN
turnover NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Poor ADJ
processes NOUN
handling NOUN
, PUNCT
difficult ADJ
company NOUN
culture NOUN
, PUNCT
people NOUN
leaving VERB
all DET
the DET
time NOUN
--------------------------------------------------
Tokenization and POS Tagging:
There PRON
are VERB
no DET
cons NOUN
here ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Leadership PROPN
has VERB
some DET
hiccups NOUN
regarding VERB
transparency NOUN
--------------------------------------------------
Tokenization and POS Tagging:
no DET
bonuses NOUN
SPACE
not PART
allowed VERB
to PART
work VERB
from ADP
another DET
country NOUN
like ADP
many ADJ
other ADJ
companies NOUN
offer VERB
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
’s AUX
growing VERB
fast ADV
, PUNCT
maybe ADV
too ADV
fast ADJ
for ADP
their PRON
own ADJ
good NOUN
. PUNCT
Quite ADV
messy ADJ
when SCONJ
it PRON
comes VERB
to ADP
roles NOUN
, PUNCT
task NOUN
, PUNCT
etc X
. X
it PRON
really ADV
depends VERB
on ADP
you PRON
Manager PROPN
if SCONJ
your PRON
team NOUN
has VERB
a DET
clear ADJ
objective NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
1 X
. PUNCT
Understaffed ADJ
teams NOUN
SPACE
2 NUM
. PUNCT
Major ADJ
setbacks NOUN
from ADP
lay NOUN
- PUNCT
offs NOUN
SPACE
3 NUM
. PUNCT
Unclear PROPN
ESPO PROPN
policy NOUN
SPACE
4 NUM
. PUNCT
You PRON
may AUX
spend VERB
more ADJ
time NOUN
on ADP
probation NOUN
that SCONJ
you PRON
imagine VERB
Named Entity Recognition:
1 CARDINAL
Understaffed PRODUCT
2 CARDINAL
3 CARDINAL
4 CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Too ADV
Frequent ADJ
changes NOUN
in ADP
leadership NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Extra ADJ
hours NOUN
, PUNCT
unstable ADJ
and CCONJ
incoherent ADJ
invoriment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
seeing VERB
any DET
cons NOUN
here ADV
--------------------------------------------------
Tokenization and POS Tagging:
Fighting VERB
with ADP
regulatory ADJ
aspects NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
There PRON
are VERB
many ADJ
growing VERB
pains NOUN
still ADV
and CCONJ
more ADV
senior ADJ
people NOUN
with ADP
diverse ADJ
backgrounds NOUN
/ SYM
experience NOUN
need VERB
to PART
be AUX
pulled VERB
in ADP
to PART
drive VERB
this DET
organisation NOUN
SPACE
Cross ADJ
- ADJ
functional ADJ
support NOUN
/ SYM
ownership NOUN
is AUX
n't PART
organised VERB
well ADV
and CCONJ
you PRON
'll AUX
often ADV
be AUX
passed VERB
along ADP
to ADP
someone PRON
else ADV
to ADP
no DET
avail NOUN
SPACE
Does AUX
n't PART
really ADV
feel VERB
like SCONJ
the DET
founders NOUN
care VERB
that SCONJ
much ADV
about ADP
employees NOUN
( PUNCT
they PRON
lack VERB
empathy ADJ
signalling VERB
) PUNCT
Named Entity Recognition:
Cross ORG
--------------------------------------------------
Tokenization and POS Tagging:
For ADP
now ADV
, PUNCT
there PRON
are VERB
many ADJ
people NOUN
leaving VERB
the DET
company NOUN
and CCONJ
I PRON
believe VERB
that SCONJ
interin NOUN
positions NOUN
are AUX
not PART
the DET
answer NOUN
for ADP
Turn PROPN
Over ADP
--------------------------------------------------
Tokenization and POS Tagging:
crazy ADJ
politics NOUN
and CCONJ
unfair ADJ
practices NOUN
SPACE
nepotism NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Not PART
very ADV
competitive ADJ
salaries NOUN
and CCONJ
career NOUN
, PUNCT
not PART
very ADV
good ADJ
retention NOUN
program NOUN
--------------------------------------------------
Tokenization and POS Tagging:
lack NOUN
of ADP
consistency NOUN
in ADP
management NOUN
decisions NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Seemed VERB
chaotic/ PROPN
was AUX
n't PART
much ADJ
training NOUN
at ADP
all ADV
for ADP
new ADJ
interns NOUN
. PUNCT
Would AUX
hope VERB
that SCONJ
by ADP
now ADV
there PRON
is VERB
a DET
more ADV
systematic ADJ
training NOUN
program NOUN
in ADP
place NOUN
for ADP
new ADJ
employees NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Work NOUN
life NOUN
balance NOUN
is AUX
not PART
great ADJ
--------------------------------------------------
Tokenization and POS Tagging:
The DET
principal ADJ
engineers NOUN
are AUX
absolutely ADV
the DET
worst ADJ
engineers NOUN
in ADP
the DET
whole ADJ
company NOUN
. PUNCT
SPACE
Nobody PRON
knows VERB
how SCONJ
they PRON
’ve AUX
been AUX
promoted VERB
, PUNCT
they PRON
were AUX
previously ADV
very ADV
toxic ADJ
and CCONJ
incompetent ADJ
tech NOUN
lesds NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Only ADV
possible ADJ
work NOUN
from ADP
office NOUN
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
can AUX
be AUX
chaotic ADJ
sometimes ADV
. PUNCT
SPACE
Miscommunication PROPN
can AUX
happen VERB
, PUNCT
you PRON
have VERB
to PART
just ADV
stay VERB
on ADP
top NOUN
of ADP
things NOUN
and CCONJ
double ADJ
check NOUN
. PUNCT
SPACE
You PRON
have VERB
to PART
love VERB
this DET
fast ADV
paced VERB
environment NOUN
, PUNCT
as SCONJ
it PRON
can AUX
be AUX
challenging VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
The DET
only ADJ
Cons PROPN
where SCONJ
more ADV
due ADJ
to ADP
the DET
pandemic ADJ
--------------------------------------------------
Tokenization and POS Tagging:
the DET
salary NOUN
is AUX
low ADJ
SPACE
they PRON
are AUX
not PART
hands NOUN
- PUNCT
on ADP
--------------------------------------------------
Tokenization and POS Tagging:
The DET
company NOUN
is AUX
very ADV
fast ADJ
changing VERB
, PUNCT
so ADV
you PRON
need VERB
to PART
be AUX
prepared ADJ
to PART
follow VERB
and CCONJ
adapt VERB
to ADP
it PRON
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
You PRON
need VERB
to PART
be AUX
able ADJ
to PART
find VERB
your PRON
way NOUN
in ADP
a DET
fast ADV
- PUNCT
paced ADJ
and CCONJ
fast ADV
- PUNCT
changing VERB
environment NOUN
. PUNCT
SPACE
- PUNCT
A DET
lot NOUN
of ADP
work NOUN
to PART
answer VERB
ongoing ADJ
challenges NOUN
for ADP
neobanks NOUN
and CCONJ
fintech NOUN
companies NOUN
( PUNCT
compensated VERB
by ADP
great ADJ
opportunities NOUN
to PART
learn VERB
, PUNCT
great ADJ
colleagues NOUN
and CCONJ
good ADJ
remuneration NOUN
packages NOUN
) PUNCT
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
bad ADJ
to PART
say VERB
, PUNCT
I PRON
like VERB
to PART
be AUX
here ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
More ADJ
pressure NOUN
in ADP
terms NOUN
of ADP
work NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Confusing ADJ
communication NOUN
from ADP
leadership NOUN
. PUNCT
They PRON
really ADV
need VERB
to PART
have VERB
more ADJ
realist ADJ
goals NOUN
instead ADV
of ADP
putting VERB
their PRON
teams NOUN
under ADP
a DET
huge ADJ
presure NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Lacking VERB
overall ADJ
team NOUN
spirit NOUN
SPACE
Little ADJ
to ADP
no DET
contact NOUN
with ADP
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Intransparent ADJ
leadership NOUN
SPACE
- PUNCT
Lack NOUN
of ADP
focus NOUN
and CCONJ
strategy NOUN
SPACE
- PUNCT
No PRON
career NOUN
development NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Chaotic ADJ
nature NOUN
, PUNCT
incentivization NOUN
and CCONJ
reward NOUN
wildly ADV
differ VERB
, PUNCT
big ADJ
cultural ADJ
shifts NOUN
ongoing ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Some DET
processes NOUN
are AUX
still ADV
quite ADV
chaotic ADJ
and CCONJ
needs VERB
improvement NOUN
--------------------------------------------------
Tokenization and POS Tagging:
diluted ADJ
culture NOUN
SPACE
intransparent ADJ
performance NOUN
reviews NOUN
SPACE
ESOP PROPN
package NOUN
Named Entity Recognition:
ESOP ORG
--------------------------------------------------
Tokenization and POS Tagging:
Quite DET
a DET
fast ADV
- PUNCT
paced VERB
work NOUN
environment NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
In ADP
completely ADV
disarray NOUN
since SCONJ
the DET
hypergrowth NOUN
, PUNCT
common ADJ
to PART
have VERB
issues NOUN
with ADP
ownership NOUN
and CCONJ
planning NOUN
. PUNCT
SPACE
C NOUN
level NOUN
not PART
in ADP
touch NOUN
with ADP
the DET
engineering NOUN
department NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Poor ADJ
mgmt NOUN
and CCONJ
C NOUN
- PUNCT
Level NOUN
SPACE
Bad ADJ
salary NOUN
conditions NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Very ADV
poor ADJ
work NOUN
culture NOUN
, PUNCT
poor ADJ
communication NOUN
from ADP
senior ADJ
management NOUN
, PUNCT
too ADV
much ADJ
politics NOUN
and CCONJ
silo NOUN
- PUNCT
ed NOUN
work NOUN
--------------------------------------------------
Tokenization and POS Tagging:
bad ADJ
management NOUN
everyone PRON
was AUX
expendable ADJ
and CCONJ
happily ADV
rotate VERB
staff NOUN
bad ADJ
at ADP
keeping VERB
moral ADJ
--------------------------------------------------
Tokenization and POS Tagging:
Maybe ADV
the DET
tasks NOUN
can AUX
be AUX
scheduled VERB
better ADV
--------------------------------------------------
Tokenization and POS Tagging:
Some PRON
depending VERB
on ADP
the DET
position NOUN
and CCONJ
department NOUN
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
might AUX
be AUX
chaotic ADJ
sometimes ADV
and CCONJ
not PART
everyone PRON
is AUX
able ADJ
to PART
deal VERB
with ADP
it PRON
. PUNCT
SPACE
I PRON
personally ADV
love VERB
it PRON
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Founders NOUN
should AUX
trust VERB
their PRON
management NOUN
team NOUN
and CCONJ
be AUX
less ADJ
hands NOUN
- PUNCT
on NOUN
and CCONJ
top NOUN
- PUNCT
down NOUN
. PUNCT
SPACE
The DET
company NOUN
approach VERB
to ADP
remote ADJ
work NOUN
after SCONJ
covid NOUN
is AUX
absurd ADJ
, PUNCT
the DET
teams NOUN
are AUX
super ADV
productive ADJ
while SCONJ
working VERB
remotely ADV
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Lack NOUN
of ADP
growth NOUN
opportunities NOUN
SPACE
- PUNCT
Long ADJ
- PUNCT
term NOUN
employees NOUN
are AUX
not PART
valued VERB
SPACE
- PUNCT
Low PROPN
salaries NOUN
compared VERB
to ADP
what PRON
the DET
market NOUN
pays VERB
--------------------------------------------------
Tokenization and POS Tagging:
Ca AUX
n't PART
think VERB
of ADP
anything PRON
so ADV
far ADV
--------------------------------------------------
Tokenization and POS Tagging:
Too ADV
many ADJ
changes NOUN
. PUNCT
Lack NOUN
of ADP
management NOUN
or CCONJ
fair ADJ
assesment NOUN
--------------------------------------------------
Tokenization and POS Tagging:
* PUNCT
Many ADJ
big ADJ
shifts NOUN
in ADP
priority NOUN
in ADP
the DET
past NOUN
SPACE
* PUNCT
I PRON
hope VERB
we PRON
wo AUX
n't PART
repeat VERB
the DET
issues NOUN
of ADP
the DET
past NOUN
with ADP
hypergrowth NOUN
. PUNCT
At ADP
least ADJ
onboarding VERB
is AUX
definitely ADV
getting VERB
better ADJ
with ADP
time NOUN
Named Entity Recognition:
hypergrowth LANGUAGE
--------------------------------------------------
Tokenization and POS Tagging:
High ADJ
turnover NOUN
across ADP
the DET
company NOUN
--------------------------------------------------
Tokenization and POS Tagging:
-Bad PUNCT
management NOUN
SPACE
-Too PUNCT
many ADJ
changes NOUN
in ADP
hierarchy NOUN
SPACE
-Confusing VERB
responsibilities NOUN
and CCONJ
way NOUN
of ADP
working NOUN
SPACE
-Projects NOUN
run VERB
too ADV
slow ADJ
because SCONJ
of ADP
too ADV
much ADJ
stakeholders NOUN
involvements NOUN
SPACE
-Lack NOUN
of ADP
passion NOUN
within ADP
teams NOUN
SPACE
-Average ADJ
salary NOUN
SPACE
-ESOPs NOUN
instead ADV
of ADP
salary NOUN
increase NOUN
SPACE
-too PUNCT
many ADJ
office NOUN
changes NOUN
SPACE
-too PUNCT
many ADJ
team NOUN
changes NOUN
, PUNCT
hiring NOUN
and CCONJ
resigns VERB
( PUNCT
or CCONJ
firing VERB
) PUNCT
SPACE
-management PROPN
is AUX
inexperienced ADJ
SPACE
-Depending VERB
too ADV
much ADV
on ADP
external ADJ
SaaS ADJ
tools NOUN
Named Entity Recognition:
SaaS PRODUCT
--------------------------------------------------
Tokenization and POS Tagging:
Temporary ADJ
contracts NOUN
, PUNCT
high ADJ
staff NOUN
turnover NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
main ADJ
issues NOUN
at ADP
N26 PROPN
are AUX
imho ADJ
from ADP
the DET
upper ADJ
management NOUN
. PUNCT
The DET
direction NOUN
of ADP
the DET
company NOUN
is AUX
very ADV
much ADV
top NOUN
- PUNCT
down NOUN
and CCONJ
-though PUNCT
they PRON
have AUX
been AUX
trying VERB
to PART
change VERB
that- SCONJ
the DET
individual ADJ
teams NOUN
/ SYM
segments NOUN
have VERB
not PART
enough ADJ
ownership NOUN
to PART
drive VERB
the DET
initiatives NOUN
that PRON
they PRON
believe VERB
would AUX
have VERB
the DET
most ADJ
impact NOUN
. PUNCT
As ADP
a DET
result NOUN
, PUNCT
processes NOUN
become VERB
sluggish ADJ
, PUNCT
people NOUN
sometimes ADV
become VERB
less ADV
motivated ADJ
because SCONJ
to PART
achieve VERB
anything PRON
you PRON
need VERB
to PART
go VERB
through ADP
endless ADJ
loops NOUN
. PUNCT
SPACE
Furthermore ADV
the DET
employees NOUN
have VERB
little ADJ
trust NOUN
of ADP
the DET
leadership NOUN
team NOUN
. PUNCT
From ADP
my PRON
experience NOUN
, PUNCT
after ADP
a DET
while NOUN
I PRON
stop VERB
caring VERB
on ADP
what PRON
the DET
leadership NOUN
was AUX
saying VERB
and CCONJ
focused VERB
on ADP
my PRON
work NOUN
and CCONJ
team NOUN
( PUNCT
which PRON
was AUX
great ADJ
) PUNCT
. PUNCT
There PRON
's VERB
no DET
point NOUN
watching VERB
the DET
founders NOUN
Q&A PROPN
sessions NOUN
if SCONJ
all PRON
you PRON
get VERB
out ADP
of ADP
it PRON
is AUX
inconclusive ADJ
, PUNCT
ready ADJ
- PUNCT
made VERB
answers NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
nightmares NOUN
, PUNCT
bullying NOUN
, PUNCT
nepotism NOUN
, PUNCT
politics NOUN
, PUNCT
egos NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
low ADJ
pay NOUN
and CCONJ
no DET
transparency NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
no DET
bonuses NOUN
( PUNCT
even ADV
if SCONJ
we PRON
meet VERB
targets NOUN
) PUNCT
SPACE
a DET
lot NOUN
of ADP
information NOUN
to PART
assimilate VERB
and CCONJ
not PART
enough ADJ
time NOUN
given VERB
to PART
do VERB
so ADV
SPACE
procedures NOUN
are AUX
not PART
always ADV
easy ADJ
to PART
find VERB
--------------------------------------------------
Tokenization and POS Tagging:
No DET
transparency NOUN
when SCONJ
it PRON
comes VERB
to ADP
promotions NOUN
, PUNCT
contract NOUN
renewals NOUN
and CCONJ
sacked VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
to PART
report VERB
for ADP
now ADV
--------------------------------------------------
Tokenization and POS Tagging:
Aggressive PROPN
Culture PROPN
, PUNCT
no DET
direction NOUN
, PUNCT
young ADJ
leadership NOUN
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
culture NOUN
modelled VERB
by ADP
founders NOUN
is AUX
" PUNCT
success NOUN
only ADV
" PUNCT
, PUNCT
low ADJ
transparency NOUN
/ SYM
empathy ADJ
SPACE
- PUNCT
product NOUN
focus NOUN
is AUX
missing VERB
, PUNCT
investments NOUN
across ADP
many ADJ
directions NOUN
( PUNCT
rather ADV
than ADP
focussed VERB
) PUNCT
SPACE
- PUNCT
mass NOUN
exodus NOUN
of ADP
staff NOUN
that PRON
repeats VERB
every DET
2 NUM
years NOUN
Named Entity Recognition:
every 2 years DATE
--------------------------------------------------
Tokenization and POS Tagging:
Founders NOUN
are AUX
a DET
bit NOUN
conservative ADJ
regarding VERB
remote ADJ
work NOUN
. PUNCT
SPACE
I PRON
found VERB
it PRON
a DET
bit NOUN
unorganized ADJ
, PUNCT
specially ADV
on ADP
communication NOUN
between ADP
different ADJ
teams NOUN
, PUNCT
a DET
downside NOUN
of ADP
having VERB
microservices NOUN
architecture NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
No DET
work NOUN
life NOUN
balance NOUN
, PUNCT
minimal ADJ
development NOUN
options NOUN
, PUNCT
overpaid VERB
team NOUN
leads VERB
, PUNCT
pointless ADJ
performance NOUN
meetings NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Huge ADJ
staff NOUN
turnover NOUN
, PUNCT
lack NOUN
of ADP
leadership NOUN
almost ADV
everywhere ADV
, PUNCT
terrible ADJ
communication NOUN
from ADP
management NOUN
--------------------------------------------------
Tokenization and POS Tagging:
The DET
Founders NOUN
are AUX
a DET
boy NOUN
club NOUN
of ADP
Austrians PROPN
that PRON
only ADV
care VERB
about ADP
the DET
money NOUN
they PRON
will AUX
make VERB
when SCONJ
the DET
company NOUN
has VERB
an DET
IPO PROPN
and CCONJ
could AUX
care VERB
less ADJ
about ADP
the DET
employees NOUN
. PUNCT
People NOUN
leave VERB
so ADV
fast ADV
that SCONJ
no DET
one NOUN
even ADV
knows VERB
who PRON
works VERB
there ADV
anymore ADV
. PUNCT
It PRON
is AUX
ship NOUN
that PRON
is AUX
sinking VERB
so ADV
fast ADV
! PUNCT
! PUNCT
! PUNCT
What PRON
a DET
shame NOUN
. PUNCT
Named Entity Recognition:
Austrians NORP
IPO ORG
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Main ADJ
offices NOUN
are AUX
located VERB
in ADP
Berlin PROPN
SPACE
- PUNCT
Career NOUN
path NOUN
not PART
very ADV
well ADV
defined VERB
in ADP
most ADJ
teams NOUN
SPACE
- PUNCT
Often ADV
contracts NOUN
do AUX
not PART
get AUX
renewed VERB
SPACE
- PUNCT
Most ADJ
managers NOUN
lack VERB
proper ADJ
managerial ADJ
skills NOUN
SPACE
- PUNCT
Team PROPN
culture NOUN
differs VERB
vastly ADV
from ADP
one NUM
team NOUN
to ADP
another PRON
Named Entity Recognition:
Berlin GPE
one CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Management NOUN
communication NOUN
, PUNCT
interaction NOUN
between ADP
departments NOUN
--------------------------------------------------
Tokenization and POS Tagging:
CEO NOUN
is AUX
such DET
a DET
baby NOUN
, PUNCT
everyone PRON
working VERB
with ADP
him PRON
ends VERB
up ADP
quitting VERB
. PUNCT
SPACE
Domino PROPN
's PART
effect NOUN
, PUNCT
so ADV
many ADJ
people NOUN
are AUX
leaving VERB
, PUNCT
creating VERB
even ADV
more ADJ
chaos NOUN
. PUNCT
Named Entity Recognition:
Domino ORG
--------------------------------------------------
Tokenization and POS Tagging:
Constantly ADV
taking VERB
2 NUM
- SYM
3live NUM
chats NOUN
simultaneously ADV
for ADP
up ADP
to PART
7 NUM
hours NOUN
per ADP
day NOUN
. PUNCT
SPACE
All PRON
of ADP
the DET
benefits NOUN
we PRON
were AUX
promised VERB
were AUX
taken VERB
away ADP
SPACE
Customer NOUN
service NOUN
are AUX
treated VERB
like ADP
2nd ADJ
class NOUN
citizens NOUN
in ADP
the DET
company NOUN
. PUNCT
SPACE
Constantly ADV
changing VERB
processes NOUN
SPACE
False ADJ
promises NOUN
Named Entity Recognition:
2 CARDINAL
up to 7 hours TIME
2nd ORDINAL
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Co NOUN
- NOUN
founders NOUN
do AUX
n't PART
trust VERB
the DET
seasoned VERB
executives NOUN
they PRON
bring VERB
in ADP
to PART
do VERB
what PRON
they PRON
are AUX
there ADV
to PART
do VERB
SPACE
- PUNCT
Lack NOUN
of ADP
willingness NOUN
to PART
make VERB
hard ADJ
decisions NOUN
about ADP
priorities NOUN
, PUNCT
or CCONJ
delegate NOUN
decision NOUN
making NOUN
beyond ADP
the DET
" PUNCT
inner ADJ
circle NOUN
" PUNCT
of ADP
top ADJ
management NOUN
that PRON
has AUX
been AUX
there ADV
from ADP
the DET
start NOUN
SPACE
- PUNCT
That SCONJ
" PUNCT
inner ADJ
circle NOUN
" PUNCT
is AUX
completely ADV
out ADP
of ADP
touch NOUN
with ADP
what PRON
it PRON
means VERB
to PART
be AUX
one NUM
of ADP
their PRON
employees NOUN
and CCONJ
have VERB
zero NUM
empathy NOUN
for ADP
how SCONJ
their PRON
decisions NOUN
impact VERB
the DET
lives NOUN
of ADP
their PRON
employees NOUN
, PUNCT
or CCONJ
the DET
product NOUN
negatively ADV
SPACE
- PUNCT
The DET
only ADJ
way NOUN
to PART
get VERB
something PRON
done VERB
is AUX
to PART
play VERB
massive ADJ
political ADJ
games NOUN
SPACE
- PUNCT
Logic NOUN
almost ADV
never ADV
wins VERB
out ADP
Named Entity Recognition:
zero CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Founders NOUN
do AUX
n't PART
trust VERB
their PRON
team NOUN
SPACE
Marketing PROPN
leaders NOUN
always ADV
changing VERB
SPACE
Founders NOUN
only ADV
promote VERB
their PRON
friends NOUN
SPACE
Product PROPN
does AUX
not PART
innovate VERB
SPACE
All PRON
talk NOUN
, PUNCT
no DET
action NOUN
. PUNCT
Marketing NOUN
leaders NOUN
pretend VERB
to PART
be AUX
nice ADJ
. PUNCT
It PRON
’s VERB
all PRON
lip NOUN
- PUNCT
service NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Messy NOUN
organisation NOUN
at ADP
the DET
time NOUN
I PRON
was AUX
there ADV
- PUNCT
roles NOUN
and CCONJ
goals NOUN
of ADP
teams NOUN
were AUX
changing VERB
all DET
the DET
time NOUN
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
can AUX
be AUX
chaotic ADJ
but CCONJ
part NOUN
of ADP
a DET
growing VERB
company NOUN
--------------------------------------------------
Tokenization and POS Tagging:
Leadership NOUN
in ADP
HR PROPN
are AUX
extremely ADV
under ADP
qualified ADJ
, PUNCT
which PRON
leads VERB
to ADP
insecurities NOUN
and CCONJ
rewarding VERB
/ SYM
hiring VERB
only ADV
those PRON
who PRON
make VERB
them PRON
look VERB
good ADJ
. PUNCT
Looking VERB
good ADJ
is AUX
really ADV
the DET
only ADJ
thing NOUN
that PRON
matters VERB
, PUNCT
you PRON
can AUX
get VERB
away ADV
with ADP
a DET
lot NOUN
by ADP
complimenting VERB
management NOUN
in ADP
public ADJ
settings NOUN
and CCONJ
patting VERB
each DET
other ADJ
on ADP
the DET
back NOUN
for ADP
projects NOUN
that PRON
are AUX
designed VERB
only ADV
to PART
make VERB
management NOUN
look VERB
good ADJ
( PUNCT
no DET
practical ADJ
value NOUN
added VERB
) PUNCT
. PUNCT
Most ADJ
of ADP
the DET
competent ADJ
and CCONJ
driven VERB
people NOUN
had AUX
left VERB
the DET
team NOUN
in ADP
the DET
beginning NOUN
of ADP
this DET
year NOUN
because SCONJ
they PRON
were AUX
underpaid ADJ
and CCONJ
overworked VERB
, PUNCT
replaced VERB
by ADP
agency NOUN
recruiters NOUN
who PRON
care VERB
only ADV
about ADP
commission NOUN
and CCONJ
not PART
at ADV
all DET
the DET
quality NOUN
of ADP
their PRON
hires NOUN
( PUNCT
which PRON
I PRON
can AUX
imagine VERB
will AUX
have VERB
a DET
very ADV
negative ADJ
effect NOUN
down ADP
the DET
line NOUN
across ADP
company NOUN
) PUNCT
. PUNCT
I PRON
can AUX
not PART
recommend VERB
you PRON
to PART
join VERB
this DET
company NOUN
. PUNCT
Only ADV
do VERB
so ADV
if SCONJ
you PRON
must AUX
, PUNCT
it PRON
's AUX
not PART
a DET
happy ADJ
environment NOUN
. PUNCT
Named Entity Recognition:
the beginning of this year DATE
--------------------------------------------------
Tokenization and POS Tagging:
It PRON
's AUX
a DET
quickly ADV
changing VERB
place NOUN
, PUNCT
you PRON
need VERB
to PART
be AUX
adaptable ADJ
and CCONJ
have VERB
an DET
appetite NOUN
for ADP
complexity NOUN
to PART
thrive VERB
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
not PART
sure ADJ
what PRON
to PART
mention VERB
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Awful ADJ
management NOUN
! PUNCT
All DET
the DET
rumors NOUN
are AUX
true ADJ
! PUNCT
SPACE
- PUNCT
Founders NOUN
are AUX
friendly ADJ
and CCONJ
took VERB
time NOUN
to PART
discuss VERB
with ADP
their PRON
workforce NOUN
during ADP
momentum NOUN
of ADP
crisis NOUN
but CCONJ
it PRON
seems VERB
just ADV
beow VERB
them PRON
it PRON
's AUX
a DET
total ADJ
mess NOUN
. PUNCT
SPACE
- PUNCT
Low PROPN
salaries NOUN
! PUNCT
They PRON
said VERB
they PRON
have VERB
2 NUM
rounds NOUN
March PROPN
and CCONJ
September PROPN
, PUNCT
it PRON
's AUX
a DET
LIE NOUN
. PUNCT
On ADP
March PROPN
it PRON
is AUX
to PART
go VERB
on ADP
another DET
level NOUN
( PUNCT
BTW ADV
no DET
one NOUN
understand VERB
anything PRON
about ADP
this PRON
and CCONJ
when SCONJ
you PRON
ask VERB
HR PROPN
they PRON
say VERB
it PRON
's AUX
confidential ADJ
. PUNCT
Wut PROPN
? PUNCT
This DET
information NOUN
has AUX
been AUX
made VERB
public ADJ
therefore ADV
this PRON
is AUX
not PART
a DET
company NOUN
secret NOUN
anymore ADV
) PUNCT
. PUNCT
On ADP
September PROPN
they PRON
tell VERB
you PRON
that SCONJ
since SCONJ
the DET
policy NOUN
around ADP
it PRON
has AUX
changed VERB
and CCONJ
it PRON
is AUX
also ADV
" PUNCT
in ADP
relation NOUN
to ADP
the DET
general PROPN
bahaviour PROPN
" PUNCT
, PUNCT
basically ADV
you PRON
can AUX
kiss VERB
goodbye VERB
the DET
tiny ADJ
piecce NOUN
of ADP
cheese NOUN
you PRON
would AUX
get VERB
out ADP
of ADP
it PRON
anyway ADV
. PUNCT
SPACE
- PUNCT
Never ADV
integrating VERB
CS PROPN
suppot NOUN
in ADP
anything PRON
. PUNCT
Therefore ADV
you PRON
learn VERB
about ADP
major ADJ
changes NOUN
of ADP
the DET
product NOUN
sometimes ADV
the DET
same ADJ
day NOUN
as ADP
a DET
customer NOUN
. PUNCT
# SYM
adaptability NOUN
SPACE
- PUNCT
Buggy NOUN
processes NOUN
. PUNCT
Never ADV
applying VERB
feedback NOUN
. PUNCT
SPACE
- PUNCT
Firing VERB
the DET
talents NOUN
but CCONJ
outsourcing VERB
on ADP
external ADJ
centers NOUN
that PRON
do AUX
not PART
follow VERB
the DET
same ADJ
quality NOUN
standards NOUN
. PUNCT
SPACE
- PUNCT
Promoting VERB
ass NOUN
- NOUN
kissers NOUN
despite SCONJ
more ADV
capable ADJ
employees NOUN
. PUNCT
It PRON
makes VERB
you PRON
wonder VERB
who PRON
is AUX
taking VERB
the DET
decisions NOUN
upstairs ADV
. PUNCT
SPACE
- PUNCT
Departments PROPN
that PRON
delagate VERB
the DET
shitty ADJ
tasks NOUN
to PART
lower ADJ
level NOUN
because SCONJ
reasons NOUN
? PUNCT
Of ADV
course ADV
, PUNCT
consequently ADV
, PUNCT
responsabilities NOUN
are AUX
increasing VERB
on ADP
a DET
personel NOUN
level NOUN
but CCONJ
not PART
the DET
money NOUN
;) PUNCT
SPACE
- PUNCT
Q PROPN
and CCONJ
A PROPN
's PART
sessions NOUN
are AUX
the DET
best ADJ
dodging VERB
bullets NOUN
scene NOUN
I PRON
have AUX
ever ADV
seen VERB
. PUNCT
They PRON
are AUX
only ADV
good ADJ
if SCONJ
you PRON
want VERB
to PART
learn VERB
irrelevant ADJ
topics NOUN
about ADP
management NOUN
and CCONJ
how SCONJ
good ADJ
marketing NOUN
is AUX
doing VERB
. PUNCT
SPACE
- PUNCT
Forget PROPN
about ADP
evolutions NOUN
. PUNCT
You PRON
are AUX
just ADV
good ADJ
to PART
be AUX
an DET
extra ADJ
. PUNCT
Nothing PRON
more ADV
Named Entity Recognition:
2 CARDINAL
March and September DATE
LIE ORG
March DATE
Wut PERSON
September DATE
the same day DATE
--------------------------------------------------
Tokenization and POS Tagging:
Leadership NOUN
. PUNCT
Bad ADJ
behaviour NOUN
is AUX
celebrated VERB
. PUNCT
A DET
culture NOUN
of ADP
' PUNCT
visibility NOUN
' PUNCT
which PRON
leads VERB
to ADP
no DET
meritocracy NOUN
and CCONJ
no DET
benefit NOUN
of ADP
doing VERB
a DET
good ADJ
job NOUN
. PUNCT
You PRON
can AUX
say VERB
you PRON
're AUX
' PUNCT
busy ADJ
' PUNCT
and CCONJ
do VERB
nothing PRON
. PUNCT
If SCONJ
you PRON
are AUX
visible ADJ
then ADV
you PRON
are AUX
able ADJ
to PART
feel VERB
some DET
sort NOUN
of ADP
praise NOUN
. PUNCT
Otherwise ADV
, PUNCT
the DET
ones NOUN
who PRON
are AUX
working VERB
hard ADV
and CCONJ
dedicating VERB
themselves PRON
are AUX
left VERB
alone ADV
. PUNCT
SPACE
If SCONJ
anyone PRON
is AUX
looking VERB
to PART
join VERB
, PUNCT
they PRON
need VERB
to PART
establish VERB
why SCONJ
they PRON
are AUX
joining VERB
, PUNCT
you PRON
wo AUX
n't PART
be AUX
promoted VERB
or CCONJ
be AUX
given VERB
any DET
salary NOUN
increase NOUN
unless SCONJ
you PRON
sell VERB
your PRON
soul NOUN
and CCONJ
suck VERB
up ADP
to ADP
the DET
management NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
a DET
total ADJ
mess NOUN
, PUNCT
and CCONJ
unfortunately ADV
no DET
real ADJ
opportunity NOUN
to PART
grow VERB
inside ADP
the DET
company NOUN
. PUNCT
crazy ADJ
turnover NOUN
. PUNCT
--------------------------------------------------
Tokenization and POS Tagging:
Hypergrowth NOUN
, PUNCT
heavy ADJ
on ADP
medium NOUN
management NOUN
. PUNCT
Named Entity Recognition:
Hypergrowth NORP
--------------------------------------------------
Tokenization and POS Tagging:
Nothing PRON
… PUNCT
except SCONJ
those DET
works VERB
council NOUN
persons NOUN
that PRON
dare VERB
to PART
gather VERB
in ADP
bars NOUN
to PART
resist VERB
the DET
masters NOUN
. PUNCT
Treason PROPN
! PUNCT
SPACE
Good ADJ
thing NOUN
that PRON
the DET
CEO PROPN
sent VERB
the DET
cops NOUN
in ADP
time NOUN
and CCONJ
showed VERB
them PRON
who PRON
’s VERB
the DET
boss NOUN
! PUNCT
SPACE
I PRON
’m VERB
confused ADJ
about ADP
his PRON
later ADJ
tweet NOUN
tho PROPN
! PUNCT
SPACE
Still ADV
miss VERB
those DET
Amalfi PROPN
coast NOUN
CX PROPN
retreats NOUN
that PRON
we PRON
get AUX
pampered VERB
with ADP
. PUNCT
What PRON
a DET
celebration NOUN
of ADP
being AUX
in ADP
love NOUN
with ADP
ourselves PRON
! PUNCT
After ADP
all PRON
we PRON
deserve VERB
it PRON
for ADP
doing VERB
such DET
a DET
job NOUN
! PUNCT
SPACE
I PRON
have VERB
no DET
doubts NOUN
about ADP
the DET
glorious ADJ
future NOUN
! PUNCT
Running VERB
out ADP
of ADP
money NOUN
or CCONJ
flash NOUN
sale NOUN
are AUX
the DET
two NUM
success NOUN
scenarios NOUN
on ADP
the DET
table NOUN
and CCONJ
I PRON
ca AUX
n’t PART
wait VERB
to PART
give VERB
every DET
last ADJ
drop NOUN
of ADP
engineering NOUN
essence NOUN
for ADP
our PRON
leaders NOUN
pocket NOUN
and CCONJ
future ADJ
keynote NOUN
story NOUN
! PUNCT
SPACE
Je PROPN
t’aime PROPN
N26 PROPN
! PUNCT
Named Entity Recognition:
Amalfi GPE
two CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Lack NOUN
of ADP
consistent ADJ
work NOUN
hours NOUN
, PUNCT
changed VERB
from ADP
week NOUN
to ADP
week NOUN
. PUNCT
Named Entity Recognition:
hours TIME
week to week DATE
--------------------------------------------------
Tokenization and POS Tagging:
- PUNCT
Working NOUN
overtime NOUN
is AUX
required VERB
on ADP
a DET
daily ADJ
basis NOUN
SPACE
- PUNCT
Must AUX
prove VERB
yourself PRON
and CCONJ
show VERB
extreme ADJ
loyalty NOUN
to PART
earn VERB
a DET
permanent ADJ
contract NOUN
SPACE
- PUNCT
Very ADV
low ADJ
salary NOUN
SPACE
- PUNCT
Incompetent ADJ
managers NOUN
and CCONJ
team NOUN
leads VERB
SPACE
- PUNCT
Young ADJ
professionals NOUN
who PRON
are AUX
very ADV
inexperienced ADJ
SPACE
- PUNCT
Multiple ADJ
languages NOUN
is AUX
a DET
requirement NOUN
if SCONJ
you PRON
want VERB
to PART
succeed VERB
in ADP
a DET
role NOUN
SPACE
- PUNCT
Operations PROPN
departments NOUN
are AUX
extremely ADV
disorganized VERB
SPACE
- PUNCT
Operations PROPN
departments NOUN
are AUX
extremely ADV
understaffed ADJ
SPACE
- PUNCT
Very ADV
repetitive ADJ
tasks NOUN
that PRON
take VERB
a DET
toll NOUN
on ADP
mental ADJ
health NOUN
SPACE
- PUNCT
Saturday PROPN
work NOUN
Named Entity Recognition:
daily DATE
Saturday DATE
--------------------------------------------------
Tokenization and POS Tagging:
Zero NUM
value NOUN
of ADP
staff NOUN
, PUNCT
experience NOUN
, PUNCT
excellence NOUN
. PUNCT
Toxic ADJ
culture NOUN
and CCONJ
environment NOUN
. PUNCT
Named Entity Recognition:
Zero CARDINAL
--------------------------------------------------
Tokenization and POS Tagging:
Top ADJ
management NOUN
made VERB
mostly ADV
of ADP
Linkedin PROPN
celebs ADJ
that PRON
are AUX
completely ADV
out ADP
of ADP
touch NOUN
from ADP
their PRON
departments NOUN
and CCONJ
too ADV
busy ADJ
managing VERB
up ADP
to PART
be AUX
able ADJ
to PART
understand VERB
how SCONJ
their PRON
teams NOUN
work VERB
. PUNCT
This PRON
causes VERB
a DET
lot NOUN
of ADP
frustration NOUN
because SCONJ
many ADJ
times NOUN
decisions NOUN
are AUX
taken VERB
without ADP
any DET
knowledge NOUN
on ADP
how SCONJ
they PRON
operationally ADV
and CCONJ
structurally ADV
affect VERB
the DET
teams NOUN
and CCONJ
the DET
product NOUN
. PUNCT
Named Entity Recognition:
Linkedin ORG
--------------------------------------------------
Tokenization and POS Tagging:
Communication NOUN
between ADP
higher ADJ
level NOUN
management NOUN
and CCONJ
team NOUN
is AUX
bad ADJ
SPACE
Employees NOUN
not PART
valued VERB
--------------------------------------------------
Tokenization and POS Tagging:
not PART
much ADJ
flexibility NOUN
in ADP
schedule NOUN
; PUNCT
baseline NOUN
pay NOUN
to PART
afford VERB
living VERB
in ADP
the DET
area NOUN
; PUNCT
often ADV
made VERB
to PART
work VERB
overtime NOUN
without ADP
being AUX
compensated VERB
, PUNCT
but CCONJ
then ADV
scolded VERB
for ADP
needing VERB
time NOUN
to PART
leave VERB
early/ ADJ
for ADP
appointments/ PROPN
etc NOUN
. X
; PUNCT
told VERB
we PRON
were AUX
promoted VERB
and CCONJ
allowed VERB
to PART
take VERB
on ADP
new ADJ
role NOUN
, PUNCT
but CCONJ
waited VERB
months NOUN
for SCONJ
it PRON
to PART
become VERB
official ADJ
- PUNCT
paid VERB
same ADJ
wage NOUN
while SCONJ
working VERB
a DET
higher ADJ
up ADJ
role NOUN
for ADP
nearly ADV
6 NUM
months NOUN
Named Entity Recognition:
months DATE
nearly 6 months DATE
--------------------------------------------------
Tokenization and POS Tagging:
Low ADJ
salary NOUN
, PUNCT
a DET
bit NOUN
messy ADJ
for ADP
process NOUN
--------------------------------------------------
Tokenization and POS Tagging:
This PRON
has AUX
been AUX
the DET
experience NOUN
for ADP
the DET
last ADJ
20 NUM
months NOUN
and CCONJ
I PRON
do AUX
n't PART
see VERB
any DET
positive ADJ
change NOUN
: PUNCT
SPACE
Very ADV
bad ADJ
politics NOUN
SPACE
Below ADP
market NOUN
salary NOUN
SPACE
Upper PROPN
management NOUN
do AUX
n’t PART
care VERB
about ADP
employees NOUN
or CCONJ
retention NOUN
rates NOUN
. PUNCT
SPACE
Lack NOUN
of ADP
recognition NOUN
of ADP
good ADJ
work NOUN
. PUNCT
SPACE
Lots NOUN
of ADP
chaos NOUN
due ADP
to ADP
lack NOUN
of ADP
transparency NOUN
, PUNCT
decision NOUN
making NOUN
and CCONJ
high ADJ
attrition NOUN
rates NOUN
. PUNCT
SPACE
Lots NOUN
of ADP
disappointment NOUN
and CCONJ
difficult ADJ
to PART
grow VERB
ones NOUN
career NOUN
internally ADV
. PUNCT
SPACE
Promotions NOUN
without ADP
getting VERB
a DET
raise NOUN
. PUNCT
SPACE
Lots NOUN
of ADP
pressure NOUN
to PART
be AUX
compliant ADJ
but CCONJ
very ADV
little ADJ
support NOUN
to PART
make VERB
it PRON
feasible ADJ
SPACE
Budget PROPN
constraints NOUN
and CCONJ
limited ADJ
resources NOUN
. PUNCT
SPACE
Unachievable PROPN
deadlines NOUN
SPACE
Under ADP
qualified ADJ
management NOUN
with ADP
very ADV
little ADJ
experience NOUN
SPACE
Constant ADJ
process NOUN
changes NOUN
which PRON
makes VERB
everything PRON
complicated ADJ
and CCONJ
take VERB
extra ADV
long ADV
- PUNCT
very ADV
difficult ADJ
to PART
get VERB
stuff NOUN
done VERB
SPACE
People NOUN
team NOUN
is AUX
there ADV
to PART
support VERB
the DET
founders NOUN
, PUNCT
not PART
to PART
make VERB
employee NOUN
experience NOUN
good ADJ
Named Entity Recognition:
the last 20 months DATE
Upper ORG
Constant PERSON
--------------------------------------------------
At this stage, I performed text preprocessing on the 'Pros' and 'Cons' reviews for both Deutsche Bank and N26 datasets.
First, I loaded the spaCy model for English language processing (en_core_web_md). Then, I defined a preprocessing function called preprocess_text, which applies several transformations to the text by converting text to lowercase, removing numbers, removing hyphens and replace with space, removing punctuation, replacing multiple whitespaces with a single space, and striping any leading/trailing whitespace. After that, I used spaCy for tokenization and stop word removal which filters out tokens that are stop words, punctuation, whitespace, single characters, numeric-like tokens, and currency symbols. Lastly, I also excluded pronouns and then joined the tokens back into a string to form cleaned text.
Finally, the function is applied to the 'Pros' and 'Cons' columns of both Deutsche Bank (db) and N26 (n26) datasets, and the cleaned text is stored in new columns named 'Pros_cleaned' and 'Cons_cleaned' for further analysis.
This preprocessing step prepares the text data for subsequent analysis tasks, such as sentiment analysis or topic modeling, by removing noise and irrelevant information.
# Load the spaCy model for English language
nlp = spacy.load('en_core_web_md')
# Define the preprocessing function
def preprocess_text(text):
# Convert text to lowercase
text = text.lower()
# Remove numbers
text = re.sub(r'\d+', ' ', text)
# Remove hyphens and replace with space
text = re.sub(r'-', ' ', text)
# Remove punctuation
text = re.sub(r'[^\w\s]', '', text)
# Replace multiple whitespaces with a single space
text = re.sub(r'\s+', ' ', text)
# Strip any leading/trailing whitespace that may have appeared
text = text.strip()
# Use spaCy for tokenization and stop word removal
doc = nlp(text)
# Filter out tokens that are stop words, punctuation, whitespace, or single characters, and convert to lowercase
tokens = [token.lemma_ for token in doc
if not token.is_stop
and not token.is_punct
and not token.is_space
and not token.is_digit
and not token.like_num # Check for numeric-like tokens
and not token.is_currency
and token.lemma_ != '-PRON-'
and len(token.text) > 1]
# Join the tokens back into a string
clean_text = ' '.join(tokens)
return clean_text
db['Pros_cleaned'] = db['Pros'].apply(preprocess_text)
db['Cons_cleaned'] = db['Cons'].apply(preprocess_text)
n26['Pros_cleaned'] = n26['Pros'].apply(preprocess_text)
n26['Cons_cleaned'] = n26['Cons'].apply(preprocess_text)
db.head(10)
| Employee Title | Location | Pros | Cons | Rating | Company Name | Pros_cleaned | Cons_cleaned | |
|---|---|---|---|---|---|---|---|---|
| 0 | Compliance Analyst | Berlin | The environment within the team was open and n... | The temporary contract and the salary | 5.0 | Deutsche Bank | environment team open competitive people kind ... | temporary contract salary |
| 1 | Vice President M&A | Frankfurt am Main | Deutsche Bank in Germany still kind of #1, and... | Globally lacking behind BB US banks, but keeps... | 5.0 | Deutsche Bank | deutsche bank germany kind involve big deal | globally lack bb bank keep closely ensure low bb |
| 2 | Graduate Trainee In Technology/Digital | Frankfurt am Main | The job stands for a very good work-life balance. | Less challenging for career beginners. | 4.0 | Deutsche Bank | job stand good work life balance | challenging career beginner |
| 3 | Internship | Frankfurt am Main | Flexible work and 39h/ week | In Corona times difficult to network | 4.0 | Deutsche Bank | flexible work week | corona time difficult network |
| 4 | Corporate Treasury Sales | Frankfurt am Main | Trading floor experience and good socials | Monotone work and no good training | 4.0 | Deutsche Bank | trading floor experience good social | monotone work good training |
| 5 | Risk Analyst | Berlin | Flexible working hours, good work-life balance | There is not many Career Development Opportuni... | 4.0 | Deutsche Bank | flexible working hour good work life balance | career development opportunity |
| 6 | Bank Assistant | Hamburg | Helpful for everyone job seeker | i dont know anything about it thank you so much | 4.0 | Deutsche Bank | helpful job seeker | not know thank |
| 7 | Analyst | Frankfurt am Main | Friendly and helpful environment, good payment | Heavy workload sometimes 100h weeks \nLittle t... | 4.0 | Deutsche Bank | friendly helpful environment good payment | heavy workload week little understanding workl... |
| 8 | CIB Operation Regional Lead | Frankfurt am Main | Work Life balance, Pay is okay | Internal promotion is difficult Politics | 4.0 | Deutsche Bank | work life balance pay okay | internal promotion difficult politic |
| 9 | Internship | Frankfurt am Main | Facilities, relaxed working hours, opportunity... | Responsibility, a lot of admin work | 2.0 | Deutsche Bank | facility relax work hour opportunity look team... | responsibility lot admin work |
n26.head(10)
| Employee Title | Location | Pros | Cons | Rating | Company Name | Pros_cleaned | Cons_cleaned | |
|---|---|---|---|---|---|---|---|---|
| 0 | Anonymous Employee | Berlin | Good teams and setups, solid Ways of working | Management crisis, high turnover on management... | 3.0 | N26 | good team setup solid way work | management crisis high turnover management lev... |
| 1 | Senior Software Engineer | Berlin | Work environment relaxed, even better after th... | Management is not really ready to listen opini... | 2.0 | N26 | work environment relax well works council ente... | management ready listen opinion different thei... |
| 2 | Anonymous Employee | Berlin | The teams have some of the nicest and hard wor... | The managers in this company do not support th... | 1.0 | N26 | team nice hard work people | manager company support wonderful people contr... |
| 3 | User Researcher | Berlin | Diverse work, lot's of in-house talent | Some uncertainty with organisation direction | 4.0 | N26 | diverse work lot house talent | uncertainty organisation direction |
| 4 | Analytics Engineer | Berlin | Wonderful people to work with and state of the... | Project workflow can be lost in the thread whe... | 3.0 | N26 | wonderful people work state art tech stack | project workflow lose thread change happen eas... |
| 5 | International Customer Support Specialist | Berlin | Good place to work in general | Not so much chance of growth | 3.0 | N26 | good place work general | chance growth |
| 6 | Compliance Specialist | Berlin | the company still is quite interesting | the salary is quite low | 3.0 | N26 | company interesting | salary low |
| 7 | Product Designer | Berlin | Great people. Fun product. Learnt so much | Too many layers in the org after scaling a lot... | 5.0 | N26 | great people fun product learn | layer org scale lot quickly |
| 8 | Backend Engineer | Berlin | good vibes\ngood peoples\nnice office in the c... | problems with budget regarding salary updates | 4.0 | N26 | good vibe good people nice office center berli... | problem budget salary update |
| 9 | Senior Product Analyst | Berlin | Full Remote work. The product analytics team i... | Product leaders need more focus on being more ... | 3.0 | N26 | remote work product analytic team nice support... | product leader need focus datum inform datum i... |
For this step, I would like to show the total word count in the 'Pros_cleaned' and 'Cons_cleaned' columns for Deutsche Bank and N26 after preprocessing.** It splits each review into words and sums up the word counts in both 'Pros_cleaned' and 'Cons_cleaned' columns separately for each company.
# Total word count in 'Pros' and 'Cons'
db_wordcount_pros_post = db['Pros_cleaned'].apply(lambda x: len(x.split())).sum()
db_wordcount_cons_post = db['Cons_cleaned'].apply(lambda x: len(x.split())).sum()
n26_wordcount_pros_post = n26['Pros_cleaned'].apply(lambda x: len(x.split())).sum()
n26_wordcount_cons_post = n26['Cons_cleaned'].apply(lambda x: len(x.split())).sum()
print(f"Total word count in 'Pros_cleaned' reviews of Deutsche Bank after preprocessing: {db_wordcount_pros_post} words")
print(f"Total word count in 'Cons_cleaned' reviews of Deutsche Bank after preprocessing: {db_wordcount_cons_post} words")
print(f"Total word count in 'Pros_cleaned' reviews of N26 after preprocessing: {n26_wordcount_pros_post} words")
print(f"Total word count in 'Cons_cleaned' reviews of N26 after preprocessing: {n26_wordcount_cons_post} words")
Total word count in 'Pros_cleaned' reviews of Deutsche Bank after preprocessing: 1309 words Total word count in 'Cons_cleaned' reviews of Deutsche Bank after preprocessing: 1327 words Total word count in 'Pros_cleaned' reviews of N26 after preprocessing: 2081 words Total word count in 'Cons_cleaned' reviews of N26 after preprocessing: 2328 words
The output indicates that after preprocessing, there are 1309 words in the 'Pros_cleaned' column and 1327 words in the 'Cons_cleaned' column for Deutsche Bank, while for N26, there are 2081 words in the 'Pros_cleaned' column and 2328 words in the 'Cons_cleaned' column.
This step computes the mean length of each review by applying the len() function to each text and then the output shows the average length of 'Pros_cleaned' and 'Cons_cleaned' columns, measured in terms of the number of characters after preprocessing for both Deutsche Bank and N26 datasets.
# Mean length of reviews (in terms of number of characters)
db_meanlength_pros_post = db['Pros_cleaned'].apply(len).mean()
db_meanlength_cons_post = db['Cons_cleaned'].apply(len).mean()
n26_meanlength_pros_post = n26['Pros_cleaned'].apply(len).mean()
n26_meanlength_cons_post = n26['Cons_cleaned'].apply(len).mean()
print(f"Average length of 'Pros_cleaned' reviews of Deutsche Bank after preprocessing: {db_meanlength_pros_post} characters")
print(f"Average length of 'Cons_cleaned' reviews of Deutsche Bank after preprocessing: {db_meanlength_cons_post} characters")
print(f"Average length of 'Pros_cleaned' reviews of N26 after preprocessing: {n26_meanlength_pros_post} characters")
print(f"Average length of 'Cons_cleaned' reviews of N26 after preprocessing: {n26_meanlength_cons_post} characters")
Average length of 'Pros_cleaned' reviews of Deutsche Bank after preprocessing: 46.72 characters Average length of 'Cons_cleaned' reviews of Deutsche Bank after preprocessing: 47.35 characters Average length of 'Pros_cleaned' reviews of N26 after preprocessing: 73.545 characters Average length of 'Cons_cleaned' reviews of N26 after preprocessing: 84.825 characters
The results show that, on average, 'Pros_cleaned' and 'Cons_cleaned' reviews for Deutsche Bank have a length of approximately 46.72 characters and 47.35 characters, respectively, before preprocessing. For N26, the average length of 'Pros_cleaned' and 'Cons_cleaned' reviews is approximately 73.545 characters and 84.825 characters, respectively, after preprocessing.
After preprocessing, I calculated the average word length in 'Pros_cleaned' and 'Cons_cleaned' reviews after preprocessing for both Deutsche Bank and N26 datasets.** I applied a lambda function to each entry in the 'Pros_cleaned' and 'Cons_cleaned' columns of both datasets to split the text into words and then calculated the mean length of these words, considering only alphabetic characters.
# Average word length in 'Pros' and 'Cons'
db_avg_wordlength_pros_post = db['Pros_cleaned'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
db_avg_wordlength_cons_post = db['Cons_cleaned'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
n26_avg_wordlength_pros_post = n26['Pros_cleaned'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
n26_avg_wordlength_cons_post = n26['Cons_cleaned'].apply(lambda x: np.mean([len(word) for word in x.split() if word.isalpha()])).mean()
print(f"Average word length in 'Pros_cleaned' reviews of Deutsche Bank after preprocessing: {db_avg_wordlength_pros_post}")
print(f"Average word length in 'Cons_cleaned' reviews of Deutsche Bank after preprocessing: {db_avg_wordlength_cons_post}")
print(f"Average word length in 'Pros_cleaned' reviews of N26 after preprocessing: {n26_avg_wordlength_pros_post}")
print(f"Average word length in 'Cons_cleaned' reviews of N26 after preprocessing: {n26_avg_wordlength_cons_post}")
Average word length in 'Pros_cleaned' reviews of Deutsche Bank after preprocessing: 6.248046072237576 Average word length in 'Cons_cleaned' reviews of Deutsche Bank after preprocessing: 6.256767754089016 Average word length in 'Pros_cleaned' reviews of N26 after preprocessing: 6.207176135831977 Average word length in 'Cons_cleaned' reviews of N26 after preprocessing: 6.373807051294438
The results indicate that, on average, words in 'Pros_cleaned' reviews for Deutsche Bank have a length of approximately 6.25 characters, while words in 'Cons_cleaned' reviews have a length of approximately 6.26 characters after preprocessing. For N26, the average word length in 'Pros_cleaned' reviews is approximately 6.21 characters, and in 'Cons_cleaned' reviews, it is approximately 6.37 characters after preprocessing.
Then, I generated word clouds for the positive and negative reviews (‘Pros_cleaned’ and ‘Cons_cleaned’) of both Deutsche Bank and N26 datasets by using WordCloud library. The word clouds visually represent the most frequent words in the positive and negative reviews, providing a quick overview of the sentiments expressed by employees towards each company.
# Combine all pros into a single string
db_pros_text = " ".join(review for review in db.Pros_cleaned.dropna())
# Generate a word cloud image for pros
db_wordcloud_pros = WordCloud(background_color="white", colormap='viridis').generate(db_pros_text)
# Combine all cons into a single string
db_cons_text = " ".join(review for review in db.Cons_cleaned.dropna())
# Generate a word cloud image for cons
db_wordcloud_cons = WordCloud(background_color="white", colormap='magma').generate(db_cons_text)
# Create subplots with 1 row and 2 columns
fig, axes = plt.subplots(1, 2, figsize=(15, 5))
# Plotting word cloud for pros
axes[0].imshow(db_wordcloud_pros, interpolation='bilinear')
axes[0].set_title('Word Cloud from Positive Reviews (Pros_cleaned) in Deutsche Bank')
axes[0].axis("off")
# Plotting word cloud for cons
axes[1].imshow(db_wordcloud_cons, interpolation='bilinear')
axes[1].set_title('Word Cloud From Negative Reviews (Cons_cleaned) in Deutsche Bank')
axes[1].axis("off")
plt.tight_layout()
plt.show()
For Deutsche Bank, the positive reviews highlight "good", "work", "life", "balance", and "team", suggesting employees appreciate the work environment and work-life balance. The negative reviews emphasize "work", "process", "slow", "career", and "bureaucracy", indicating that areas of dissatisfaction often relate to workload, career progression, and bureaucratic hurdles..
# Combine all pros into a single string
n26_pros_text = " ".join(review for review in n26.Pros_cleaned.dropna())
# Generate a word cloud image for pros
n26_wordcloud_pros = WordCloud(background_color="white", colormap='viridis').generate(n26_pros_text)
# Combine all cons into a single string
n26_cons_text = " ".join(review for review in n26.Cons_cleaned.dropna())
# Generate a word cloud image for cons
n26_wordcloud_cons = WordCloud(background_color="white", colormap='magma').generate(n26_cons_text)
# Create subplots with 1 row and 2 columns
fig, axes = plt.subplots(1, 2, figsize=(15, 5))
# Plotting word cloud for pros
axes[0].imshow(n26_wordcloud_pros, interpolation='bilinear')
axes[0].set_title('Word Cloud from Positive Reviews (Pros_cleaned) in N26')
axes[0].axis("off")
# Plotting word cloud for cons
axes[1].imshow(n26_wordcloud_cons, interpolation='bilinear')
axes[1].set_title('Word Cloud from Negative Reviews (Cons_cleaned) in N26')
axes[1].axis("off")
plt.tight_layout()
plt.show()
In the positive reviews of N26, words such as "good", "work", "great", "team", and "environment" stand out, suggesting that employees value the workplace culture and team dynamics. The negative reviews emphasize "management", "change", "salary", "company", and "team", pointing to concerns with management practices, salary satisfaction, and the company's approach to change.
Overall, both companies show positive feedback on the “team” and “people”. Noticeably, “work-life balance” is prominent in the positive aspects of Deutsche Bank, while “slow work process” and "management" are negative points for Deutsche Bank and N26, respectively.
In this step, I would like to show bar charts of the most common words found in the positive and negative reviews of Deutsche Bank and N26.
# Load English tokenizer, tagger, parser, NER, and word vectors
nlp = spacy.load("en_core_web_md")
# Function to preprocess text with spaCy (tokenization and stop word removal)
def preprocess(text):
doc = nlp(text)
# Include additional condition to ignore tokens that are empty strings or spaces
return [token.text.lower() for token in doc if not token.is_stop and not token.is_punct and not token.is_space and token.text.strip()]
# Apply the preprocessing to Pros and Cons and count words
db_pros_words = [word for review in db.Pros_cleaned.dropna() for word in preprocess(review)]
db_pros_words = Counter(db_pros_words)
db_cons_words = [word for review in db.Cons_cleaned.dropna() for word in preprocess(review)]
db_cons_words = Counter(db_cons_words)
# Directly get the 10 most common words and their counts for Pros and Cons
db_pros_mostcommon = db_pros_words.most_common(10)
db_cons_mostcommon = db_cons_words.most_common(10)
# Unpack the words and their frequencies
db_pros_words, db_pros_counts = zip(*db_pros_mostcommon)
db_cons_words, db_cons_counts = zip(*db_cons_mostcommon)
# Visualization
fig, axes = plt.subplots(1, 2, figsize=(15, 5))
# Bar plot for the most common words in Pros
axes[0].bar(db_pros_words, db_pros_counts, color='darkblue')
axes[0].set_title('Most Common Words in Positive of Deutsche Bank')
axes[0].tick_params(axis='x', rotation=45)
axes[0].set_ylabel('Frequency')
# Annotate each bar with its value for Deutsche Bank
for i, value in enumerate(db_pros_counts):
axes[0].text(i, value, str(value), ha='center', va='bottom')
# Bar plot for the most common words in Cons
axes[1].bar(db_cons_words, db_cons_counts, color='darkred')
axes[1].set_title('Most Common Words in Cons of Deutsche Bank')
axes[1].tick_params(axis='x', rotation=45)
axes[1].set_ylabel('Frequency')
# Annotate each bar with its value for Deutsche Bank
for i, value in enumerate(db_cons_counts):
axes[1].text(i, value, str(value), ha='center', va='bottom')
plt.tight_layout() # Adjust layout to not overlap
plt.show()
In positive reviews of Deutsche Bank, the most frequent words are "good" (88 mentions), "work" (72), "balance" (38), "life" (32), and "great" (29), reflecting a positive sentiment towards work-life balance and the general work environment. Meanwhile, negative reviews related to "work" (46 mentions) is the most common word, followed by "slow" (26), "process" (21), and "company" (18). This suggests criticism is focused on the pace and processes within the company, and possibly the impact on employees' work.
# Load English tokenizer, tagger, parser, NER, and word vectors
nlp = spacy.load("en_core_web_md")
# Function to preprocess text with spaCy (tokenization and stop word removal)
def preprocess(text):
doc = nlp(text)
# Include additional condition to ignore tokens that are empty strings or spaces
return [token.text.lower() for token in doc if not token.is_stop and not token.is_punct and token.text.strip()]
# Apply the preprocessing to Pros and Cons and count words
n26_pros_words = [word for review in n26.Pros_cleaned.dropna() for word in preprocess(review)]
n26_pros_words = Counter(n26_pros_words)
n26_cons_words = [word for review in n26.Cons_cleaned.dropna() for word in preprocess(review)]
n26_cons_words = Counter(n26_cons_words)
# Directly get the 10 most common words and their counts for Pros and Cons
n26_pros_mostcommon = n26_pros_words.most_common(10)
n26_cons_mostcommon = n26_cons_words.most_common(10)
# Unpack the words and their frequencies
n26_pros_words, n26_pros_counts = zip(*n26_pros_mostcommon)
n26_cons_words, n26_cons_counts = zip(*n26_cons_mostcommon)
# Visualization
fig, axes = plt.subplots(1, 2, figsize=(15, 5))
# Bar plot for the most common words in Pros
axes[0].bar(n26_pros_words, n26_pros_counts, color='seagreen')
axes[0].set_title('Most Common Words in Pros of N26')
axes[0].tick_params(axis='x', rotation=45)
axes[0].set_ylabel('Frequency')
# Annotate each bar with its value for N26
for i, value in enumerate(n26_pros_counts):
axes[0].text(i, value, str(value), ha='center', va='bottom')
# Bar plot for the most common words in Cons
axes[1].bar(n26_cons_words, n26_cons_counts, color='indianred')
axes[1].set_title('Most Common Words in Cons of N26')
axes[1].tick_params(axis='x', rotation=45)
axes[1].set_ylabel('Frequency')
# Annotate each bar with its value for N26
for i, value in enumerate(n26_cons_counts):
axes[1].text(i, value, str(value), ha='center', va='bottom')
plt.tight_layout() # Adjust layout to not overlap
plt.show()
N26’s positive reviews most frequently include "work" (89 mentions), "great" (65), "good" (61), and "team" (56), indicating that employees have a positive perception of their work, team, and the overall company culture. Meanwhile, negative reviews related to "management" (58 mentions) is the most cited issue, followed by "work" (43), "team" (38), and "company" (28), pointing towards concerns primarily related to management and team dynamics.
These charts provide a concise quantitative analysis of common themes in employee feedback, highlighting the strengths and areas for improvement for each company as perceived by their employees.
For a topic modeling analysis, I applied Latent Dirichlet Allocation (LDA) to positive and negative employee reviews of Deutsche Bank and N26 for identifying common themes in both positive (‘Pros_cleaned’) and negative (‘Cons_cleaned’) feedback of both companies.
# Combine the preprocessed text into lists
db_pros_text = [preprocess(review) for review in db['Pros_cleaned'].dropna()]
db_cons_text = [preprocess(review) for review in db['Cons_cleaned'].dropna()]
# Create a Gensim dictionary from the text
db_pros_dictionary = Dictionary(db_pros_text)
db_cons_dictionary = Dictionary(db_cons_text)
# Filter out extremes to limit the number of features
db_pros_dictionary.filter_extremes(no_below=5, no_above=0.5)
db_cons_dictionary.filter_extremes(no_below=5, no_above=0.5)
# Convert the dictionary to a bag of words corpus
db_pros_corpus = [db_pros_dictionary.doc2bow(text) for text in db_pros_text]
db_cons_corpus = [db_cons_dictionary.doc2bow(text) for text in db_cons_text]
# Train LDA model for Pros_cleaned and Cons_cleaned
db_pros_topics = LdaModel(corpus=db_pros_corpus, id2word=db_pros_dictionary, num_topics=3, random_state=42, passes=10, iterations=50)
db_cons_topics = LdaModel(corpus=db_cons_corpus, id2word=db_cons_dictionary, num_topics=3, random_state=42, passes=10, iterations=50)
# Display topics for Pros_cleaned and Cons_cleaned
print("Positive Topics in Deutsche Bank:")
for idx, topic in db_pros_topics.print_topics(-1):
print(f"Topic: {idx} \nWords: {topic}\n")
print("Negative Topics in Deutsche Bank:")
for idx, topic in db_cons_topics.print_topics(-1):
print(f"Topic: {idx} \nWords: {topic}\n")
Positive Topics in Deutsche Bank: Topic: 0 Words: 0.205*"good" + 0.056*"team" + 0.053*"benefit" + 0.050*"nice" + 0.047*"environment" + 0.043*"salary" + 0.041*"company" + 0.040*"employee" + 0.037*"people" + 0.034*"management" Topic: 1 Words: 0.107*"opportunity" + 0.089*"people" + 0.074*"lot" + 0.067*"great" + 0.056*"environment" + 0.055*"international" + 0.050*"work" + 0.044*"interesting" + 0.042*"bank" + 0.041*"career" Topic: 2 Words: 0.199*"work" + 0.111*"balance" + 0.102*"good" + 0.102*"life" + 0.051*"great" + 0.050*"colleague" + 0.038*"culture" + 0.036*"office" + 0.036*"project" + 0.032*"nice" Negative Topics in Deutsche Bank: Topic: 0 Words: 0.117*"slow" + 0.076*"process" + 0.074*"career" + 0.054*"bureaucracy" + 0.049*"salary" + 0.035*"pay" + 0.033*"good" + 0.033*"new" + 0.033*"old" + 0.028*"year" Topic: 1 Words: 0.099*"work" + 0.063*"bad" + 0.052*"promotion" + 0.049*"life" + 0.049*"balance" + 0.045*"employee" + 0.044*"low" + 0.043*"management" + 0.039*"con" + 0.038*"change" Topic: 2 Words: 0.151*"work" + 0.085*"lot" + 0.062*"company" + 0.060*"people" + 0.053*"big" + 0.048*"team" + 0.045*"time" + 0.044*"hour" + 0.034*"office" + 0.034*"environment"
The LDA analysis on Deutsche Bank's employee reviews reveals three main topics in both positive and negative feedback.
Positive Feedback Topics:
Team and Benefits: This topic features "good," "team," and "benefit" show that employees are happy with the teamwork and benefits provided.
Work Opportunities and Environment: With "opportunity," "international," and "environment", it shows that employees appreciate the career opportunities and the international work environment.
Work-life Balance: Words such as "work," "balance," and "life," indicate a positive sentiment about managing professional and personal life effectively.
Negative Feedback Topics:
Slow Career Progression and Bureaucracy Systems: The words "slow," "process," "career," and "bureaucracy" point to frustrations with slow career advancement and bureaucracy processes.
Work-life Imbalance: The prominence of "work," "bad," "life," and "balance," reflects dissatisfaction with managing their personnal life with work.
Workload and Company Structure: Words such as "Work," "lot," "company," and "big" highlight challenges with heavy workloads and the impact of the company's large size.
In summary, employees of Deutsche Bank appreciate the teamwork, benefits, international work environment, and work-life balance. However, they express concerns about slow career progression, bureaucracy management, workload and the stress of large company structures on their work experiences.
# Combine the preprocessed text into lists
n26_pros_text = [preprocess(review) for review in n26['Pros_cleaned'].dropna()]
n26_cons_text = [preprocess(review) for review in n26['Cons_cleaned'].dropna()]
# Create a Gensim dictionary from the text
n26_pros_dictionary = Dictionary(n26_pros_text)
n26_cons_dictionary = Dictionary(n26_cons_text)
# Filter out extremes to limit the number of features
n26_pros_dictionary.filter_extremes(no_below=5, no_above=0.5)
n26_cons_dictionary.filter_extremes(no_below=5, no_above=0.5)
# Convert the dictionary to a bag of words corpus
pros_corpus = [n26_pros_dictionary.doc2bow(text) for text in n26_pros_text]
cons_corpus = [n26_cons_dictionary.doc2bow(text) for text in n26_cons_text]
# Train LDA model for Pros_cleaned and Cons_cleaned
n26_pros_topic = LdaModel(corpus=pros_corpus, id2word=n26_pros_dictionary, num_topics=3, random_state=42, passes=10, iterations=50)
n26_cons_topic = LdaModel(corpus=cons_corpus, id2word=n26_cons_dictionary, num_topics=3, random_state=42, passes=10, iterations=50)
# Display topics for Pros_cleaned and Cons_cleaned
print("Positive Topics in N26:")
for idx, topic in n26_pros_topic.print_topics(-1):
print(f"Topic: {idx} \nWords: {topic}\n")
print("Negative Topics in N26:")
for idx, topic in n26_cons_topic.print_topics(-1):
print(f"Topic: {idx} \nWords: {topic}\n")
Positive Topics in N26: Topic: 0 Words: 0.143*"work" + 0.078*"good" + 0.071*"great" + 0.051*"team" + 0.044*"people" + 0.037*"opportunity" + 0.034*"place" + 0.027*"environment" + 0.025*"employee" + 0.024*"growth" Topic: 1 Words: 0.067*"people" + 0.053*"great" + 0.052*"good" + 0.042*"lot" + 0.034*"learn" + 0.034*"colleague" + 0.033*"year" + 0.031*"benefit" + 0.030*"product" + 0.030*"high" Topic: 2 Words: 0.087*"environment" + 0.077*"team" + 0.069*"nice" + 0.069*"lot" + 0.031*"great" + 0.030*"work" + 0.026*"culture" + 0.025*"flexibility" + 0.025*"salary" + 0.025*"people" Negative Topics in N26: Topic: 0 Words: 0.065*"management" + 0.064*"level" + 0.060*"bad" + 0.059*"employee" + 0.045*"high" + 0.034*"pay" + 0.034*"chaotic" + 0.032*"task" + 0.031*"work" + 0.030*"turnover" Topic: 1 Words: 0.083*"company" + 0.058*"management" + 0.056*"work" + 0.035*"environment" + 0.032*"fast" + 0.032*"communication" + 0.029*"people" + 0.026*"culture" + 0.026*"leave" + 0.023*"employee" Topic: 2 Words: 0.084*"team" + 0.052*"management" + 0.051*"change" + 0.038*"salary" + 0.036*"work" + 0.033*"department" + 0.031*"good" + 0.030*"founder" + 0.030*"lot" + 0.028*"people"
From the LDA analysis of N26’s employee reviews, there are three main topics in both positive and negative terms.
Positive Feedback Topics:
Good Teamwork: Most employees frequently mentioned "work," "good," "great," "team," and "people," reflecting that employees have satisfaction on their work and appreciation for people and teamwork.
Learning and Growth: With "people," "learn," and "colleague," this theme shows a positive view of personal development and the chance to learn from others within the company.
Work Environment and Compensation: This topic features "environment," "nice," and "salary" indicates that employees are happy with their work environment and have satisfaction on the compensation.
Negative Feedback Topics:
Management and Organizational Challenges: The words "management," "level," "high," and "turnover," reflect challenges with management and organizational structure that might be causing frustration among employees and high turnover.
Company Dynamics and Communication: The prominence of words "company," "fast," and "communication" highlight issues with rapid company changes and potentially lacking communication.
Team Management Changes and Salary: With "team," "management," "change," and "salary,", it shows concerns about the impact of fast changes within the team or company and dissatisfaction on salary.
In conclusion, while N26’s employees feel positive about teamwork, work environment, and compensation schemes that supports personal and professional growth, they also express concerns on management practices, organizational structure, rapid company changes, cultural dynamics, and deficiencies in communication. Additionally, dissatisfaction with salary levels can lead to a high turnover rate within the organization.
In this step, I applied text clustering on the 'Pros_cleaned' data from the 'db' dataset, using the TF-IDF (Term Frequency-Inverse Document Frequency) representation with ‘ngram_range’ covering single words, bigrams, and trigrams. The KMeans clustering algorithm was then employed to group the textual data into distinct clusters based on their semantic similarities.
# Initialize TfidfVectorizer with ngram_range for single words, bigrams, and trigrams
db_pros_vectorizer_tfidf = TfidfVectorizer(ngram_range=(1, 3), max_features=5)
# Fit and transform the entire preprocessed text data into TF-IDF model
db_pros_tfidf_matrix = db_pros_vectorizer_tfidf.fit_transform(db['Pros_cleaned'])
# Define the number of clusters
db_pros_num_clusters_tfidf = 5
# Initialize KMeans with the desired number of clusters
kmeans = KMeans(n_clusters=db_pros_num_clusters_tfidf, random_state=42)
# Fit KMeans on the TF-IDF data
kmeans.fit(db_pros_tfidf_matrix)
# Predict the cluster labels for each document
db_pros_clusters_tfidf = kmeans.labels_
# Add cluster labels to your DataFrame
db['db_Pros_cluster'] = db_pros_clusters_tfidf
# Print texts belonging to a specific cluster
for i in range(db_pros_num_clusters_tfidf):
print(f"Cluster {i} texts:")
print(db[db['db_Pros_cluster'] == i]['Pros_cleaned'].head())
print("\n")
Cluster 0 texts: 0 environment team open competitive people kind ... 1 deutsche bank germany kind involve big deal 6 helpful job seeker 10 amazing worklife balance flexibility 11 international team great guidance manager inte... Name: Pros_cleaned, dtype: object Cluster 1 texts: 31 good atmosphere work 34 good pay work expectation work culture 41 great leader technology division nice colleagu... 51 good people work env 87 mention people amazing devs open space feel li... Name: Pros_cleaned, dtype: object Cluster 2 texts: 2 job stand good work life balance 5 flexible working hour good work life balance 8 work life balance pay okay 12 stable employment good work life balance 19 good work life balance Name: Pros_cleaned, dtype: object Cluster 3 texts: 3 flexible work week 9 facility relax work hour opportunity look team... 28 choose work choose goal care health bieng 29 excellent breadth work bank size global footprint 42 hybrid work style great people Name: Pros_cleaned, dtype: object Cluster 4 texts: 4 trading floor experience good social 7 friendly helpful environment good payment 14 good access core business 16 good pay flex working hour culture multi natio... 21 special good people Name: Pros_cleaned, dtype: object
Then, I performed text clustering on the same column and dataset by using ‘CountVectorizer’ or the Bag-of-Words (BoW) representation with the same ‘ngram_range’ to compare the effectiveness of these two approaches in clustering the text data.*
# Initialize CountVectorizer with ngram_range for single words, bigrams, and trigrams
db_pros_vectorizer_bow = CountVectorizer(ngram_range=(1, 3), max_features=5)
# Fit and transform the 'Pros_cleaned' text data into bag-of-words model
db_pros_bow_matrix = db_pros_vectorizer_bow.fit_transform(db['Pros_cleaned'])
# Get the feature names (i.e., the words or bigrams)
feature_names = db_pros_vectorizer_bow.get_feature_names_out()
# Convert feature names from dict_keys object to list
feature_names = list(feature_names)
# Define the number of clusters
db_pros_num_clusters_bow = 5
# Initialize KMeans with the desired number of clusters
kmeans = KMeans(n_clusters=db_pros_num_clusters_bow, random_state=42)
# Fit KMeans on the bag-of-words data
kmeans.fit(db_pros_bow_matrix)
# Predict the cluster labels for each document
db_pros_clusters_bow = kmeans.labels_
# Add cluster labels to your DataFrame
db['db_Pros_cluster_bow'] = db_pros_clusters_bow
for i in range(db_pros_num_clusters_bow):
print(f"Cluster {i} texts:")
print(db[db['db_Pros_cluster_bow'] == i]['Pros_cleaned'].head())
print("\n")
Cluster 0 texts: 0 environment team open competitive people kind ... 1 deutsche bank germany kind involve big deal 3 flexible work week 6 helpful job seeker 10 amazing worklife balance flexibility Name: Pros_cleaned, dtype: object Cluster 1 texts: 33 good benefit good support hr technology good w... 119 good culture good work good project 122 good work life balance good work culture 192 good benefit good work life balance Name: Pros_cleaned, dtype: object Cluster 2 texts: 9 facility relax work hour opportunity look team... 31 good atmosphere work 34 good pay work expectation work culture 41 great leader technology division nice colleagu... 51 good people work env Name: Pros_cleaned, dtype: object Cluster 3 texts: 4 trading floor experience good social 7 friendly helpful environment good payment 14 good access core business 16 good pay flex working hour culture multi natio... 21 special good people Name: Pros_cleaned, dtype: object Cluster 4 texts: 2 job stand good work life balance 5 flexible working hour good work life balance 8 work life balance pay okay 12 stable employment good work life balance 19 good work life balance Name: Pros_cleaned, dtype: object
Then, I applied the elbow method to represent how the within-cluster sum of squares (WCSS) decreases as the number of clusters increases and find the optimal number of clusters, for two different text data vectorizations: TFIDF (blue line) and BoW (green line).
plt.figure(figsize=(20, 8)) # Set a larger figure size for two subplots
# Calculate WCSS for TFIDF
wcss_tfidf = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=42)
kmeans.fit(db_pros_tfidf_matrix)
wcss_tfidf.append(kmeans.inertia_)
# Calculate WCSS for BoW
wcss_bow = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=42)
kmeans.fit(db_pros_bow_matrix)
wcss_bow.append(kmeans.inertia_)
# First subplot for TFIDF
plt.subplot(1, 2, 1)
plt.plot(range(1, 11), wcss_tfidf, marker='o', color='blue', label='TFIDF')
plt.title('The Elbow Method (TFIDF) for Positive Reviews of Deutsche Bank')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.legend()
# Second subplot for BoW
plt.subplot(1, 2, 2)
plt.plot(range(1, 11), wcss_bow, marker='o', color='green', label='BoW')
plt.title('The Elbow Method (BoW) for Positive Reviews of Deutsche Bank')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.legend()
plt.tight_layout()
plt.show()
From both graphs, it shows that the optimal number of clusters is 5 as a suitable choice for both vectorization methods (TFIDF and BoW), indicating that increasing the number of clusters beyond 5 will not yield significantly more distinguished.
To compare the effectiveness of these two approaches in clustering the text data, I calculated silhouette scores for each representation. The silhouette scores provide insights into the quality of clustering achieved with each representation. The silhouette score ranges from -1 to 1, where a score closer to 1 indicates that the object is well-clustered, a score around 0 indicates overlapping clusters, and a negative score indicates that the object may be assigned to the wrong cluster.
# Calculate silhouette score for TFIDF and BoW
db_pros_score_tfidf = silhouette_score(db_pros_tfidf_matrix, db_pros_clusters_tfidf)
print(f"Silhouette Score (TFIDF): {db_pros_score_tfidf}")
db_pros_score_bow = silhouette_score(db_pros_bow_matrix, db_pros_clusters_bow)
print(f"Silhouette Score (BoW): {db_pros_score_bow}")
Silhouette Score (TFIDF): 0.8456017640971961 Silhouette Score (BoW): 0.6590811531491754
Based on the results shown, higher silhouette scores generally indicate better clustering quality, suggesting that the TF-IDF representation, with a score of ~0.85, may have led to more distinct and well-separated clusters compared to the BoW representation, with a score of ~0.66.
After that, I generated word clouds for each cluster in the dataset based on the TF-IDF and BoW representations of the ‘Pros_cleaned’ data to represent key themes and topics within each cluster, offering insights into employees feedback for organizational improvements.
# Combine all text in each cluster into a single string
db_pros_clustertexts_tfidf = ["".join(db[db['db_Pros_cluster'] == i]['Pros_cleaned']) for i in range(db_pros_num_clusters_tfidf)]
plt.figure(figsize=(15, 10))
for i in range(db_pros_num_clusters_tfidf):
wordcloud_tfidf = WordCloud(width=800, height=400, background_color='white', colormap= 'viridis').generate(db_pros_clustertexts_tfidf[i])
plt.subplot(3, 3, i+1) # Adjust subplot parameters as per your number of clusters
plt.imshow(wordcloud_tfidf, interpolation='bilinear')
plt.title(f'Cluster {i} Word Cloud (TFIDF)')
plt.axis('off')
plt.show()
As shown, cluster 0 focuses on teamwork and the work opportunities, cluster 1 emphasizes good colleagues and management, cluster 2 highlights work-life balance and benefits, cluster 3 points to diverse and flexible work environment, and cluster 4 mentions good working experiences.
# Combine all text in each cluster into a single string
db_pros_clustertexts_bow = ["".join(db[db['db_Pros_cluster_bow'] == i]['Pros_cleaned']) for i in range(db_pros_num_clusters_bow)]
plt.figure(figsize=(15, 10))
for i in range(db_pros_num_clusters_bow):
wordcloud_bow = WordCloud(width=800, height=400, background_color='white', colormap= 'viridis').generate(db_pros_clustertexts_bow[i])
plt.subplot(3, 3, i+1) # Adjust subplot parameters as per your number of clusters
plt.imshow(wordcloud_bow, interpolation='bilinear')
plt.title(f'Cluster {i} Word Cloud (BoW)')
plt.axis('off')
plt.show()
In these word clouds, cluster 0 highlights teamwork and work opportunities, cluster 1 reflects benefits and work-life balance, cluster 2 points to good colleagues and work atmosphere, cluster 3 mentions good working experiences and good environment and cluster 4 emphasizes work-life balance. However, it is noticeable that there are some overlapping topics such as work-life balance in clusters 1 and 4. Therefore, I could safely say that the BoW approach in this case is not showing the uniqueness of the text data as strong as the TF-IDF.
After conducting text clustering using TF-IDF and Bag-of-Words (BoW) vectorizations, KMeans clustering algorithm with the elbow method, calculating silhouette scores, and generating a series of word clouds, it appears that TF-IDF vectorization is better at capturing the nuances and uniqueness of the text data, which is particularly useful in sentiment analysis. Therefore, I will use TF-IDF vectorization for the subsequent sentiment analysis of the remaining job reviews.
In the next step, I generated summaries for each cluster, highlighting the key values expressed in the positive reviews of Deutsche Bank.
# Tokenize and preprocess texts in each cluster
db_pros_cluster_texts = [db[db['db_Pros_cluster'] == i]['Pros_cleaned'] for i in range(db_pros_num_clusters_tfidf)]
db_pros_cluster_keywords = []
for texts in db_pros_cluster_texts:
# Concatenate all texts in the cluster
cluster_text = ' '.join(texts)
# Tokenize and preprocess the concatenated text
cluster_tokens = preprocess(cluster_text)
# Extract unigrams, bigrams, and trigrams
n = 3 # You can adjust this value to get n-grams of different lengths
db_pros_cluster_ngrams = list(ngrams(cluster_tokens, n))
# Count n-gram frequencies
ngram_freq = Counter(db_pros_cluster_ngrams)
# Select top keywords (you can adjust the number of keywords as needed)
top_keywords = ngram_freq.most_common(5) # Select top 10 n-grams
# Append selected keywords to the cluster_keywords list
db_pros_cluster_keywords.append([keyword for keyword, _ in top_keywords])
# Create cluster summaries based on selected keywords
db_pros_cluster_summaries = []
for i, keywords_list in enumerate(db_pros_cluster_keywords):
# Extract only the n-grams from each tuple in keywords_list
keywords = [' '.join(keyword) for keyword in keywords_list]
db_pros_summary = f"Cluster {i} Summary: Individuals in this cluster value {', '.join(keywords)}."
db_pros_cluster_summaries.append(db_pros_summary)
# Print cluster summaries
for db_pros_summary in db_pros_cluster_summaries:
print(db_pros_summary)
Cluster 0 Summary: Individuals in this cluster value steep learning curve, environment team open, team open competitive, open competitive people, competitive people kind. Cluster 1 Summary: Individuals in this cluster value good atmosphere work, atmosphere work good, work good pay, good pay work, pay work expectation. Cluster 2 Summary: Individuals in this cluster value work life balance, good work life, life balance good, balance good work, life balance work. Cluster 3 Summary: Individuals in this cluster value flexible work week, work week facility, week facility relax, facility relax work, relax work hour. Cluster 4 Summary: Individuals in this cluster value good benefit good, good culture good, trading floor experience, floor experience good, experience good social.
As shown, cluster 0 highlights growth opportunities and a supportive team environment. Meanwhile, cluster 1 mentions a positive work atmosphere and fair compensation. Cluster 2 emphasizes work-life balance, while cluster 3 appreciates flexible work arrangements. Finally, cluster 4 points to benefits, fostering a positive culture, and providing enriching experiences.
Then, I created a visualization to show the distribution of positive comments at Deutsche Bank across different clusters.
# Count the number of data points in each cluster
db_pros_cluster_counts = db['db_Pros_cluster'].value_counts()
# Define custom colors
blue_custom_colors = plt.cm.Blues(np.linspace(0.2, 1, 10))
darkblue_custom_color = ['darkblue' for _ in range(len(db_pros_cluster_counts))]
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))
# Create a pie chart in the first and second subplot
axes[0].pie(db_pros_cluster_counts.values, labels=db_pros_cluster_counts.index, autopct='%1.1f%%', startangle=140, colors=blue_custom_colors)
axes[0].set_title('Proportion of Data Points in Clusters\nof Positive Comments in Deutsche Bank')
axes[0].axis('equal')
bars = axes[1].bar(db_pros_cluster_counts.index, db_pros_cluster_counts.values, color=darkblue_custom_color)
axes[1].set_xlabel('Cluster')
axes[1].set_ylabel('Number of Data Points')
axes[1].set_title('Distribution of Data Points in Clusters\nof Positive Comments in Deutsche Bank')
for bar in bars:
yval = bar.get_height()
axes[1].text(bar.get_x() + bar.get_width()/2, yval, f'{yval}', ha='center', va='bottom')
plt.tight_layout()
plt.show()
This visual analysis shows that Cluster 0 holds the majority with 48% or 96 comments, implying that employees appreciate teamwork and the work opportunities the most. Clusters 2 and 4 have a substantial amount of comments, with 32 (16%) and 42 (21%),respectively, reflecting that employees have work-life balance and good working experiences.
Then, I performed text clustering on the 'Cons_cleaned' data from the 'db' dataset using the TF-IDF representation with the same ‘ngram range’ as before, including the elbow method and silhouette score. Moreover, I generated wordclouds and word summaries for each cluster to show the key themes of the negative reviews from their Deutsche Bank’s employees.
# Initialize TfidfVectorizer with ngram_range for single words, bigrams, and trigrams
db_cons_vectorizer = TfidfVectorizer(ngram_range=(1, 3), max_features=5)
# Fit and transform the entire preprocessed text data into TF-IDF model for Cons
db_cons_matrix = db_cons_vectorizer.fit_transform(db['Cons_cleaned'])
# Define the number of clusters
db_cons_num_clusters = 6
# Initialize KMeans with the desired number of clusters
kmeans = KMeans(n_clusters=db_cons_num_clusters, random_state=42)
# Fit KMeans on the TF-IDF data
kmeans.fit(db_cons_matrix)
# Predict the cluster labels for each document
db_cons_clusters = kmeans.labels_
# Add cluster labels to your DataFrame
db['db_Cons_cluster'] = db_cons_clusters
# Sample analysis: Print texts belonging to a specific cluster
for i in range(db_cons_num_clusters):
print(f"Cluster {i} texts:")
print(db[db['db_Cons_cluster'] == i]['Cons_cleaned'].head())
print("\n")
Cluster 0 texts: 16 usual large bank like bureaucracy poor hr proc... 19 process long include hire horizontal mobility ... 57 chaotic process forever 74 complex system slow process 79 lot process slow Name: Cons_cleaned, dtype: object Cluster 1 texts: 0 temporary contract salary 1 globally lack bb bank keep closely ensure low bb 3 corona time difficult network 6 not know thank 7 heavy workload week little understanding workl... Name: Cons_cleaned, dtype: object Cluster 2 texts: 34 exactly expect big company hard upwards 46 bad experience company 60 big company bureaucracy finance sector mean se... 62 big company process bureaucratic 84 ofcourse big company Name: Cons_cleaned, dtype: object Cluster 3 texts: 4 monotone work good training 9 responsibility lot admin work 17 work hour typical ib hour 22 challagne work life balance etc 30 alot work hour Name: Cons_cleaned, dtype: object Cluster 4 texts: 2 challenging career beginner 5 career development opportunity 14 little mentorship vague career prospect 47 unfortunately salty high range company field d... 52 slow career progression hard promote Name: Cons_cleaned, dtype: object Cluster 5 texts: 12 slow growth need push hard thing move 18 slow move easily burn devoted innovate establi... 25 slow promotion clear communication promotion b... 36 slow movement new technology stack date 44 slow upper level Name: Cons_cleaned, dtype: object
plt.figure(figsize=(20, 8)) # Set a larger figure size for two subplots
# Calculate WCSS for TFIDF
wcss_tfidf = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=42)
kmeans.fit(db_cons_matrix)
wcss_tfidf.append(kmeans.inertia_)
# Plot the elbow curve for TFIDF
plt.plot(range(1, 11), wcss_tfidf, marker='o', color='blue', label='TF-IDF')
# Add labels and title
plt.title('The Elbow Method (TF-IDF) for Negative Reviews of Deutsche Bank')
plt.xlabel('Number of Clusters')
plt.ylabel('Within-Cluster Sum of Squares (WCSS)')
plt.legend()
plt.tight_layout()
plt.show()
# Calculate silhouette score
db_cons_score = silhouette_score(db_cons_matrix, db_cons_clusters)
print(f"Silhouette Score (TFIDF): {db_cons_score}")
Silhouette Score (TFIDF): 0.883859153834744
Based on the elbow method and silhouette score analysis, I selected 6 clusters to categorize the negative reviews of Deutsche Bank, achieving a silhouette score of approximately 0.88.
Then, I created word clouds and top-5-word summaries of each cluster to discover the prominent themes among the negative feedback from N26’s employees.
# Combine all text in each cluster into a single string
db_cons_clusters = ["".join(db[db['db_Cons_cluster'] == i]['Cons_cleaned']) for i in range(db_cons_num_clusters)]
# Generate word clouds for each cluster
plt.figure(figsize=(15, 10))
for i in range(db_cons_num_clusters):
# Check if the cluster has any words
if db_cons_clusters[i]:
wordcloud = WordCloud(width=800, height=400, background_color='white', colormap='magma').generate(db_cons_clusters[i])
plt.subplot(3, 3, i+1) # Adjust subplot parameters as per your number of clusters
plt.imshow(wordcloud, interpolation='bilinear')
plt.title(f'Cluster {i} Word Cloud (TFIDF)')
plt.axis('off')
else:
print(f"Cluster {i} has no words.")
plt.show()
# Tokenize and preprocess texts in each cluster
db_cons_cluster_texts = [db[db['db_Cons_cluster'] == i]['Cons_cleaned'] for i in range(db_cons_num_clusters)]
db_cons_cluster_keywords = []
for texts in db_cons_cluster_texts:
# Concatenate all texts in the cluster
cluster_text = ' '.join(texts)
# Tokenize and preprocess the concatenated text
cluster_tokens = preprocess(cluster_text)
# Extract unigrams, bigrams, and trigrams
n = 3 # You can adjust this value to get n-grams of different lengths
db_cons_cluster_ngrams = list(ngrams(cluster_tokens, n))
# Count n-gram frequencies
ngram_freq = Counter(db_cons_cluster_ngrams)
# Select top keywords (you can adjust the number of keywords as needed)
top_keywords = ngram_freq.most_common(5) # Select top 10 n-grams
# Append selected keywords to the cluster_keywords list
db_cons_cluster_keywords.append([keyword for keyword, _ in top_keywords])
# Create cluster summaries based on selected keywords
db_cons_cluster_summaries = []
for i, keywords_list in enumerate(db_cons_cluster_keywords):
# Extract only the n-grams from each tuple in keywords_list
keywords = [' '.join(keyword) for keyword in keywords_list]
db_cons_summary = f"Cluster {i} Summary: Individuals in this cluster value {', '.join(keywords)}."
db_cons_cluster_summaries.append(db_cons_summary)
# Print cluster summaries
for db_cons_summary in db_cons_cluster_summaries:
print(db_cons_summary)
Cluster 0 Summary: Individuals in this cluster value usual large bank, large bank like, bank like bureaucracy, like bureaucracy poor, bureaucracy poor hr. Cluster 1 Summary: Individuals in this cluster value promotion low pay, temporary contract salary, contract salary globally, salary globally lack, globally lack bb. Cluster 2 Summary: Individuals in this cluster value exactly expect big, expect big company, big company hard, company hard upwards, hard upwards bad. Cluster 3 Summary: Individuals in this cluster value work life balance, bad work life, monotone work good, work good training, good training responsibility. Cluster 4 Summary: Individuals in this cluster value challenging career beginner, career beginner career, beginner career development, career development opportunity, development opportunity little. Cluster 5 Summary: Individuals in this cluster value slow growth need, growth need push, need push hard, push hard thing, hard thing slow.
As you can see, cluster 0 shows dissatisfaction with typical large bank bureaucracy and poor HR practices. Cluster 1 emphasizes concerns about low pay, temporary contracts, and a lack of promotion opportunities. Cluster 2 expresses disappointment in unrealistic expectations and challenges within a big company. Cluster 3 identifies issues with work-life balance. Cluster 4 expresses frustrations with limited career development opportunities. Finally, cluster 5 highlights concerns about slow career growth and the need for more challenging opportunities.
Then, I created the visualization to illustrate the distribution of Deutsche Bank’s negative comments across various clusters.
# Count the number of data points in each cluster
db_cons_cluster_counts = db['db_Cons_cluster'].value_counts()
# Define custom colors for the pie chart
red_custom_colors = plt.cm.Reds(np.linspace(0.2, 1, 10))
# Define custom colors for the bar chart
darkred_custom_color = ['darkred' for _ in range(len(db_cons_cluster_counts))]
# Create a 1x2 subplot layout
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))
# Create a pie chart in the first subplot
axes[0].pie(db_cons_cluster_counts.values, labels=db_cons_cluster_counts.index, autopct='%1.1f%%', startangle=140, colors=red_custom_colors)
axes[0].set_title('Proportion of Data Points in Clusters\nof Negative Comments in Deutsche Bank')
axes[0].axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
# Create a bar plot in the second subplot
bars = axes[1].bar(db_cons_cluster_counts.index, db_cons_cluster_counts.values, color=darkred_custom_color)
axes[1].set_xlabel('Cluster')
axes[1].set_ylabel('Number of Data Points')
axes[1].set_title('Distribution of Data Points in Clusters\nof Negative Comments in Deutsche Bank')
# Add count and percentage labels on the bar plot
for bar in bars:
yval = bar.get_height()
axes[1].text(bar.get_x() + bar.get_width()/2, yval, f'{yval}', ha='center', va='bottom')
#percent = f'{100 * yval / len(db):.1f}%'
#axes[1].text(bar.get_x() + bar.get_width()/2, yval, f'({percent})', ha='center', va='top', color='white')
# Adjust the layout so there's no overlap
plt.tight_layout()
plt.show()
Noticeably, cluster 1 emerges with the largest share, comprising 55.5% or 111 comments, indicative of prevalent concerns regarding low pay and a lack of promotion opportunities among employees. Additionally, cluster 2 also exhibits a significant volume of comments, representing 16% or 32 comments, highlighting work-life balance as another prominent theme of dissatisfaction.
To uncover the main themes within N26's positive reviews, I conducted text clustering on the 'Pros_cleaned' data from the 'n26' dataset. Employing methods similar to those used previously, I adjusted the ngram range to include only bigrams and trigrams. This modification led to a higher silhouette score and enhanced the uniqueness of the text data.
# Initialize TfidfVectorizer with ngram_range for bigrams and trigrams
n26_pros_vectorizer = TfidfVectorizer(ngram_range=(2, 3), max_features=5)
# Fit and transform the entire preprocessed text data into TF-IDF model for Cons
n26_pros_matrix = n26_pros_vectorizer.fit_transform(n26['Pros_cleaned'])
# Define the number of clusters
n26_pros_num_clusters = 5
# Initialize KMeans with the desired number of clusters
kmeans = KMeans(n_clusters=n26_pros_num_clusters, random_state=42)
# Fit KMeans on the TF-IDF data
kmeans.fit(n26_pros_matrix)
# Predict the cluster labels for each document
n26_pros_clusters = kmeans.labels_
# Add cluster labels to your DataFrame
n26['n26_Pros_cluster'] = n26_pros_clusters
# Sample analysis: Print texts belonging to a specific cluster
for i in range(n26_pros_num_clusters):
print(f"Cluster {i} texts:")
print(n26[n26['n26_Pros_cluster'] == i]['Pros_cleaned'].head())
print("\n")
Cluster 0 texts: 0 good team setup solid way work 2 team nice hard work people 3 diverse work lot house talent 4 wonderful people work state art tech stack 6 company interesting Name: Pros_cleaned, dtype: object Cluster 1 texts: 32 great team member work 56 great team leader management 66 stable salary great team offer relocation 82 meaningful impact project responsibility scope... 95 payrise come year free lunch meal month office... Name: Pros_cleaned, dtype: object Cluster 2 texts: 1 work environment relax well works council ente... 10 company fairly diverse interesting lot profess... 43 great work environment nice people great flexi... 61 work environment leadership interesting work d... 96 nice work environment nice employee Name: Pros_cleaned, dtype: object Cluster 3 texts: 5 good place work general 64 comparison place normal salary normal benefit ... 72 great company work especially sr tdm environme... 78 great place work friendly people 92 team great love work flexible workplace homeof... Name: Pros_cleaned, dtype: object Cluster 4 texts: 31 work life balance flexibility good benefit gre... 51 young environment perfect work life balance 85 work life balance good team diversity inclusion 91 good work life balance structure 100 work life balance team support work environmen... Name: Pros_cleaned, dtype: object
plt.figure(figsize=(20, 8)) # Set a larger figure size for two subplots
# Calculate WCSS for TFIDF
wcss_tfidf = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=42)
kmeans.fit(n26_pros_matrix)
wcss_tfidf.append(kmeans.inertia_)
# Plot the elbow curve for TFIDF
plt.plot(range(1, 11), wcss_tfidf, marker='o', color='blue', label='TF-IDF')
# Add labels and title
plt.title('The Elbow Method (TF-IDF) for Positive Reviews of N26')
plt.xlabel('Number of Clusters')
plt.ylabel('Within-Cluster Sum of Squares (WCSS)')
plt.legend()
plt.tight_layout()
plt.show()
# Calculate silhouette score
n26_pros_score = silhouette_score(n26_pros_matrix, n26_pros_clusters)
print(f"Silhouette Score (TFIDF): {n26_pros_score}")
Silhouette Score (TFIDF): 0.9830744032652657
Based on the elbow method and silhouette score, 5 clusters is effective to categorize the positive reviews of N26, achieving a silhouette score of approximately 0.98.
Then, I created word clouds and top-10-word summaries of each cluster to discover the prevalent topics emerging from the positive feedback from N26’s employees.
# Combine all text in each cluster into a single string
n26_pros_cluster_texts = ["".join(n26[n26['n26_Pros_cluster'] == i]['Pros_cleaned']) for i in range(n26_pros_num_clusters)]
# Generate word clouds for each cluster
plt.figure(figsize=(15, 10))
for i in range(n26_pros_num_clusters):
wordcloud = WordCloud(width=800, height=400, background_color='white', colormap= 'viridis').generate(n26_pros_cluster_texts[i])
plt.subplot(3, 3, i+1) # Adjust subplot parameters as per your number of clusters
plt.imshow(wordcloud, interpolation='bilinear')
plt.title(f'Cluster {i} Word Cloud')
plt.axis('off')
plt.show()
# Tokenize and preprocess texts in each cluster
n26_pros_cluster_texts = [n26[n26['n26_Pros_cluster'] == i]['Pros_cleaned'] for i in range(n26_pros_num_clusters)]
n26_pros_cluster_keywords = []
for texts in n26_pros_cluster_texts:
# Concatenate all texts in the cluster
cluster_text = ' '.join(texts)
# Tokenize and preprocess the concatenated text
cluster_tokens = preprocess(cluster_text)
# Extract unigrams, bigrams, and trigrams
n = 3 # You can adjust this value to get n-grams of different lengths
n26_pros_cluster_ngrams = list(ngrams(cluster_tokens, n))
# Count n-gram frequencies
ngram_freq = Counter(n26_pros_cluster_ngrams)
# Select top keywords (you can adjust the number of keywords as needed)
top_keywords = ngram_freq.most_common(5) # Select top 10 n-grams
# Append selected keywords to the cluster_keywords list
n26_pros_cluster_keywords.append([keyword for keyword, _ in top_keywords])
# Create cluster summaries based on selected keywords
n26_pros_cluster_summaries = []
for i, keywords_list in enumerate(n26_pros_cluster_keywords):
# Extract only the n-grams from each tuple in keywords_list
keywords = [' '.join(keyword) for keyword in keywords_list]
n26_pros_summary = f"Cluster {i} Summary: Individuals in this cluster value {', '.join(keywords)}."
n26_pros_cluster_summaries.append(n26_pros_summary)
# Print cluster summaries
for n26_pros_summary in n26_pros_cluster_summaries:
print(n26_pros_summary)
Cluster 0 Summary: Individuals in this cluster value good worklife balance, team flexible working, learning curve good, steep learning curve, personal development budget. Cluster 1 Summary: Individuals in this cluster value great team member, team member work, member work great, work great team, great team leader. Cluster 2 Summary: Individuals in this cluster value great work environment, etc great work, work environment nice, nice work environment, work environment lot. Cluster 3 Summary: Individuals in this cluster value great place work, good place work, place work general, work general comparison, general comparison place. Cluster 4 Summary: Individuals in this cluster value work life balance, good team diversity, good work life, life balance flexibility, balance flexibility good.
As shown, cluster 0 reflects flexible team dynamics and opportunities for personal development. Cluster 1 appreciates the strength of teamwork, while cluster 2 highlights a supportive and pleasant work environment. Cluster 3 points N26 to a great place to work. Lastly, cluster 4 highlights work-life balance, the diversity of the team, and the flexibility provided.
Then, I created the visualization as below to illustrate the distribution of N26’s negative comments across various clusters
# Count the number of data points in each cluster
n26_pros_cluster_counts = n26['n26_Pros_cluster'].value_counts()
# Define custom colors
green_custom_colors = plt.cm.Greens(np.linspace(0.2, 1, 10))
seagreen_custom_color = ['seagreen' for _ in range(len(n26_pros_cluster_counts))]
# Create a 1x2 subplot layout
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))
# Create a pie chart in the first subplot
axes[0].pie(n26_pros_cluster_counts.values, labels=n26_pros_cluster_counts.index, autopct='%1.1f%%', startangle=140, colors=green_custom_colors)
axes[0].set_title('Proportion of Data Points in Clusters\nof Positive Comments in N26')
axes[0].axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
# Create a bar plot in the second subplot
bars = axes[1].bar(n26_pros_cluster_counts.index, n26_pros_cluster_counts.values, color=seagreen_custom_color)
axes[1].set_xlabel('Cluster')
axes[1].set_ylabel('Number of Data Points')
axes[1].set_title('Distribution of Data Point in Clusters\nof Positive Comments in N26')
# Add count and percentage labels on the bar plot
for bar in bars:
yval = bar.get_height()
axes[1].text(bar.get_x() + bar.get_width()/2, yval, f'{yval}', ha='center', va='bottom')
# Adjust the layout so there's no overlap
plt.tight_layout()
plt.show()
This visualization reveals that cluster 0 is predominant with 81% or 162 comments, indicating that employees are satisfied with flexible team dynamics and opportunities for personal development the most. Clusters 1 and 2 each account for 5% of the comments, reflecting that employees appreciate the strength of teamwork and a supportive and pleasant work environment.
Lastly, I performed text clustering on the 'Cons_cleaned' data from the 'n26' dataset with similar methods and the ngram range as previously to find the main themes within N26's positive reviews.
# Initialize TfidfVectorizer with ngram_range for bigrams and trigrams
n26_cons_vectorizer = TfidfVectorizer(ngram_range=(2, 3), max_features=5)
# Fit and transform the entire preprocessed text data into TF-IDF model for Cons
n26_cons_matrix = n26_cons_vectorizer.fit_transform(n26['Cons_cleaned'])
# Define the number of clusters
n26_cons_num_clusters = 5
# Initialize KMeans with the desired number of clusters
kmeans = KMeans(n_clusters=n26_cons_num_clusters, random_state=42)
# Fit KMeans on the TF-IDF data
kmeans.fit(n26_cons_matrix)
# Predict the cluster labels for each document
n26_cons_clusters = kmeans.labels_
# Add cluster labels to your DataFrame
n26['n26_Cons_cluster'] = n26_cons_clusters
# Sample analysis: Print texts belonging to a specific cluster
for i in range(n26_cons_num_clusters):
print(f"Cluster {i} texts:")
print(n26[n26['n26_Cons_cluster'] == i]['Cons_cleaned'].head())
print("\n")
Cluster 0 texts: 0 management crisis high turnover management lev... 1 management ready listen opinion different thei... 2 manager company support wonderful people contr... 3 uncertainty organisation direction 4 project workflow lose thread change happen eas... Name: Cons_cleaned, dtype: object Cluster 1 texts: 116 poor process handle difficult company culture ... 127 people leave company believe interin position ... 175 founder boy club austrians care money company ... 178 ceo baby work end quit dominos effect people l... 184 leadership hr extremely qualified lead insecur... Name: Cons_cleaned, dtype: object Cluster 2 texts: 18 hear not experience upper management toxic dem... 19 lot technical debt compliance requirement bure... 64 terrible management constant change team struc... 164 main issue imho upper management direction com... 199 experience month not positive change bad polit... Name: Cons_cleaned, dtype: object Cluster 3 texts: 94 little work life balance little flexibility 110 middle management head etc problematic not com... 132 work life balance great 173 work life balance minimal development option o... Name: Cons_cleaned, dtype: object Cluster 4 texts: 11 terrible management lot people department leave 34 company terrible management issue communication 67 terrible management transparency employee valu... 90 terrible management culture Name: Cons_cleaned, dtype: object
plt.figure(figsize=(20, 8)) # Set a larger figure size for two subplots
# Calculate WCSS for TFIDF
wcss_tfidf = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=42)
kmeans.fit(n26_cons_matrix)
wcss_tfidf.append(kmeans.inertia_)
# Plot the elbow curve for TFIDF
plt.plot(range(1, 11), wcss_tfidf, marker='o', color='blue', label='TF-IDF')
# Add labels and title
plt.title('The Elbow Method for TF-IDF Representation')
plt.xlabel('Number of Clusters')
plt.ylabel('Within-Cluster Sum of Squares (WCSS)')
plt.legend()
plt.tight_layout()
plt.show()
# Calculate silhouette score
n26_cons_score = silhouette_score(n26_cons_matrix, n26_cons_clusters)
print(f"Silhouette Score: {n26_cons_score}")
Silhouette Score: 0.991173165676349
Regarding the elbow method and silhouette score, 5 clusters were determined as effective for categorizing the negative reviews of N26, achieving a silhouette score of approximately 0.99.
Subsequently, I generated word clouds and summarized the top 5 words for each cluster to uncover prevalent topics within the negative feedback from N26’s employees.
# Combine all text in each cluster into a single string
n26_cons_cluster_texts = ["".join(n26[n26['n26_Cons_cluster'] == i]['Cons_cleaned']) for i in range(n26_cons_num_clusters)]
# Generate word clouds for each cluster
plt.figure(figsize=(15, 10))
for i in range(n26_cons_num_clusters):
wordcloud = WordCloud(width=800, height=400, background_color='white', colormap= 'magma').generate(n26_cons_cluster_texts[i])
plt.subplot(3, 3, i+1) # Adjust subplot parameters as per your number of clusters
plt.imshow(wordcloud, interpolation='bilinear')
plt.title(f'Cluster {i} Word Cloud')
plt.axis('off')
plt.show()
# Tokenize and preprocess texts in each cluster
n26_cons_cluster_texts = [n26[n26['n26_Cons_cluster'] == i]['Cons_cleaned'] for i in range(n26_cons_num_clusters)]
n26_cons_cluster_keywords = []
for texts in n26_cons_cluster_texts:
# Concatenate all texts in the cluster
cluster_text = ' '.join(texts)
# Tokenize and preprocess the concatenated text
cluster_tokens = preprocess(cluster_text)
# Extract unigrams, bigrams, and trigrams
n = 3 # You can adjust this value to get n-grams of different lengths
n26_cons_cluster_ngrams = list(ngrams(cluster_tokens, n))
# Count n-gram frequencies
ngram_freq = Counter(n26_cons_cluster_ngrams)
# Select top keywords (you can adjust the number of keywords as needed)
top_keywords = ngram_freq.most_common(5) # Select top 10 n-grams
# Append selected keywords to the cluster_keywords list
n26_cons_cluster_keywords.append([keyword for keyword, _ in top_keywords])
# Create cluster summaries based on selected keywords
n26_cons_cluster_summaries = []
for i, keywords_list in enumerate(n26_cons_cluster_keywords):
# Extract only the n-grams from each tuple in keywords_list
keywords = [' '.join(keyword) for keyword in keywords_list]
n26_cons_summary = f"Cluster {i} Summary: Individuals in this cluster value {', '.join(keywords)}."
n26_cons_cluster_summaries.append(n26_cons_summary)
# Print cluster summaries
for n26_cons_summary in n26_cons_cluster_summaries:
print(n26_cons_summary)
Cluster 0 Summary: Individuals in this cluster value politic work council, high employee turnover, operation department extremely, management crisis high, crisis high turnover. Cluster 1 Summary: Individuals in this cluster value poor process handle, process handle difficult, handle difficult company, difficult company culture, company culture people. Cluster 2 Summary: Individuals in this cluster value hear experience upper, experience upper management, upper management toxic, management toxic demanding, toxic demanding couple. Cluster 3 Summary: Individuals in this cluster value work life balance, little work life, life balance little, balance little flexibility, little flexibility middle. Cluster 4 Summary: Individuals in this cluster value terrible management lot, management lot people, lot people department, people department leave, department leave company.
As shown, cluster 0 reflects concerns about internal politics, high turnover rates, and crises within the management. Cluster 1 highlights criticisms on poor handling of processes, reflecting frustration with the company's workflow management and organizational culture, while cluster 2 emphasizes their experiences with upper management, highlighting toxicity and demanding behavior Cluster 3 points to lacking of work-life balance and flexibility. Lastly, cluster 4 highlights ineffective management practices and a high rate of turnover within departments.
# Count the number of data points in each cluster
n26_cons_cluster_counts = n26['n26_Cons_cluster'].value_counts()
# Define custom colors for the pie chart
red_custom_colors = plt.cm.Reds(np.linspace(0.2, 1, 10))
# Define custom colors for the bar chart
indianred_custom_color = ['indianred' for _ in range(len(n26_cons_cluster_counts))]
# Create a 1x2 subplot layout
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))
# Create a pie chart in the first subplot
axes[0].pie(n26_cons_cluster_counts.values, labels=n26_cons_cluster_counts.index, autopct='%1.1f%%', startangle=140, colors=red_custom_colors)
axes[0].set_title('Proportion of Data Points in Clusters\nof Negative Comments in N26')
axes[0].axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
# Create a bar plot in the second subplot
bars = axes[1].bar(n26_cons_cluster_counts.index, n26_cons_cluster_counts.values, color=indianred_custom_color)
axes[1].set_xlabel('Cluster')
axes[1].set_ylabel('Number of Data Points')
axes[1].set_title('Distribution of Data Points in Clusters\nof Negative Comments in N26')
# Add count and percentage labels on the bar plot
for bar in bars:
yval = bar.get_height()
axes[1].text(bar.get_x() + bar.get_width()/2, yval, f'{yval}', ha='center', va='bottom')
#percent = f'{100 * yval / len(db):.1f}%'
#axes[1].text(bar.get_x() + bar.get_width()/2, yval, f'({percent})', ha='center', va='top', color='white')
# Adjust the layout so there's no overlap
plt.tight_layout()
plt.show()
It is noticeable that cluster 0 stands out with the largest share, encompassing 91% or 162 comments, indicating that employee have concerns regarding internal politics, high turnover rates, and management crises the most. This suggests dissatisfaction with the organizational environment and instability in leadership.
For this section, I calculated the average sentiment scores for positive (‘Pros_cleaned’) and negative (‘Cons_cleaned’) comments from Deutsche Bank and N26’s employees first.
from textblob import TextBlob
def analyze_sentiment(text):
blob = TextBlob(text)
return blob.sentiment.polarity
db['Pros_Sentiment_Cleaned'] = db['Pros_cleaned'].apply(analyze_sentiment)
db['Cons_Sentiment_Cleaned'] = db['Cons_cleaned'].apply(analyze_sentiment)
n26['Pros_Sentiment_Cleaned'] = n26['Pros_cleaned'].apply(analyze_sentiment)
n26['Cons_Sentiment_Cleaned'] = n26['Cons_cleaned'].apply(analyze_sentiment)
# Calculate average sentiment score for Pros_cleaned and Cons_cleaned in both datasets
average_db_pros_sentiment = db['Pros_Sentiment_Cleaned'].mean()
average_db_cons_sentiment = db['Cons_Sentiment_Cleaned'].mean()
average_n26_pros_sentiment = n26['Pros_Sentiment_Cleaned'].mean()
average_n26_cons_sentiment = n26['Cons_Sentiment_Cleaned'].mean()
print("Average Positive Sentiment (Pros_cleaned) for Deutsche Bank:", average_db_pros_sentiment)
print("Average Positive Sentiment (Pros_cleaned) for N26:", average_n26_pros_sentiment)
print("Average Negative Sentiment (Cons_cleaned) for Deutsche Bank:", average_db_cons_sentiment)
print("Average Negative Sentiment (Cons_cleaned) for N26:", average_n26_cons_sentiment)
Average Positive Sentiment (Pros_cleaned) for Deutsche Bank: 0.3794751893939394 Average Positive Sentiment (Pros_cleaned) for N26: 0.3981644866232477 Average Negative Sentiment (Cons_cleaned) for Deutsche Bank: -0.07101827139771993 Average Negative Sentiment (Cons_cleaned) for N26: -0.006574452376800519
As you can see, the average sentiment scores for the positive reviews ('Pros_cleaned') is higher for N26 (~0.398) compared to Deutsche Bank (~0.379), indicating a more positive perception of employee satisfaction at N26 in comparison to Deutsche Bank.
On the negative side,the average sentiment scores for 'Cons_cleaned' is less negative for N26 (~0.006) than for Deutsche Bank (~0.071), indicating that negative comments about N26 are less severe or less frequent than Deutsche Bank.
Then, I created histograms to represent the distribution of sentiment scores for positive ('Pros_cleaned') and negative ('Cons_cleaned') comments from employees at Deutsche Bank and N26. These distributions can provide a visual comparison of employee satisfaction levels between the two banks.
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20,6))
# Visualize the distribution of sentiment scores for 'Pros_cleaned' in both datasets
axes[0].hist(db['Pros_Sentiment_Cleaned'], alpha=0.9, color='darkblue', label='Deutsche Bank Pros')
axes[0].hist(n26['Pros_Sentiment_Cleaned'], alpha=0.6, color='mediumseagreen', label='N26 Pros')
axes[0].set_title('Distribution of Positive Sentiment (Pros_cleaned) Scores')
axes[0].set_xlabel('Sentiment Score')
axes[0].set_ylabel('Frequency')
axes[0].legend(loc='upper right')
# Visualize the distribution of sentiment scores for 'Cons_cleaned' in both datasets
axes[1].hist(db['Cons_Sentiment_Cleaned'], alpha=0.9, color='darkblue', label='Deutsche Bank Cons')
axes[1].hist(n26['Cons_Sentiment_Cleaned'], alpha=0.6, color='mediumseagreen', label='N26 Cons')
axes[1].set_title('Distribution of Negative Sentiment (Cons_cleaned) Scores')
axes[1].set_xlabel('Sentiment Score')
axes[1].set_ylabel('Frequency')
axes[1].legend(loc='upper right')
plt.tight_layout()
plt.show()
In the 'Pros_cleaned' histogram, N26 having a slight lead in higher positive sentiment. This suggests that N26's employees might be expressing more positive sentiments in their comments.
On the 'Cons_cleaned' histogram, there is a noticeable difference; Deutsche Bank has more negative feedback compared to N26. This could imply that issues at Deutsche Bank may be more severe or more frequently mentioned than at N26.
In next step, I define a function called ‘sentiment_label’ applied to the positive ('Pros_cleaned') and negative ('Cons_cleaned') review columns of both datasets to analyze sentiment text data and return a sentiment label ('positive', 'negative', or 'neutral') based on the polarity score. The 'sentiment_label' function categorizes the polarity score of text data into three distinct labels: 'positive' if the score is greater than 0, 'negative' if it is less than 0, and 'neutral' if the score is exactly 0.
def sentiment_label(text):
score = TextBlob(text).sentiment.polarity
if score > 0:
return 'positive'
elif score < 0:
return 'negative'
else:
return 'neutral'
db['Pros_Sentiment_Label'] = db['Pros_cleaned'].apply(sentiment_label)
db['Cons_Sentiment_Label'] = db['Cons_cleaned'].apply(sentiment_label)
n26['Pros_Sentiment_Label'] = n26['Pros_cleaned'].apply(sentiment_label)
n26['Cons_Sentiment_Label'] = n26['Cons_cleaned'].apply(sentiment_label)
Then, I visualize the distribution of sentiment labels for both Deutsche Bank and N26 across positive (‘Pro_cleaned’) and negative (‘Cons_cleaned’) reviews. For readability, I set colors by green for 'positive', orange for 'neutral', and red for 'negative'.
# Count the number of data points in each cluster
db_pros_sentiment = db['Pros_Sentiment_Label'].value_counts()
n26_pros_sentiment = n26['Pros_Sentiment_Label'].value_counts()
db_cons_sentiment = db['Cons_Sentiment_Label'].value_counts()
n26_cons_sentiment = n26['Cons_Sentiment_Label'].value_counts()
# Define custom colors for the bar chart
sentiment_custom_colors = {
'positive': 'green',
'neutral': 'orange',
'negative': 'firebrick'
}
# Create a 2x2 subplot layout
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 16))
# Plot the distribution of positive sentiment for Deutsche Bank in the first subplot
db_pros_sentiment.plot(kind='bar', ax=axes[0, 0], color=[sentiment_custom_colors[label] for label in db_pros_sentiment.index])
axes[0, 0].set_title('Distribution of Positive Employee Sentiment (Pros_cleaned) while Working at Deutsche Bank')
axes[0, 0].set_xlabel('Sentiment')
axes[0, 0].set_ylabel('Count')
axes[0, 0].tick_params(axis='x', rotation=360) # Rotate x-axis labels by 360 degrees
# Add count and percentage labels on the bar plot for DB Pros
for index, value in enumerate(db_pros_sentiment.values):
axes[0, 0].text(index, value, f'{value}\n({value/sum(db_pros_sentiment.values)*100:.1f}%)', ha='center', va='bottom')
# Plot the distribution of positive sentiment for N26 in the second subplot
n26_pros_sentiment.plot(kind='bar', ax=axes[0, 1], color=[sentiment_custom_colors[label] for label in n26_pros_sentiment.index])
axes[0, 1].set_title('Distribution of Positive Employee Sentiment (Pros_cleaned) while Working at N26')
axes[0, 1].set_xlabel('Sentiment')
axes[0, 1].set_ylabel('Count')
axes[0, 1].tick_params(axis='x', rotation=360) # Rotate x-axis labels by 360 degrees
# Add count and percentage labels on the bar plot for N26 Pros
for index, value in enumerate(n26_pros_sentiment.values):
axes[0, 1].text(index, value, f'{value}\n({value/sum(n26_pros_sentiment.values)*100:.1f}%)', ha='center', va='bottom')
# Plot the distribution of negative sentiment for Deutsche Bank in the third subplot
db_cons_sentiment.plot(kind='bar', ax=axes[1, 0], color=[sentiment_custom_colors[label] for label in db_cons_sentiment.index])
axes[1, 0].set_title('Distribution of Negative Employee Sentiment (Cons_cleaned) while Working at Deutsche Bank')
axes[1, 0].set_xlabel('Sentiment')
axes[1, 0].set_ylabel('Count')
axes[1, 0].tick_params(axis='x', rotation=360) # Rotate x-axis labels by 360 degrees
# Add count and percentage labels on the bar plot for DB Cons
for index, value in enumerate(db_cons_sentiment.values):
axes[1, 0].text(index, value, f'{value}\n({value/sum(db_cons_sentiment.values)*100:.1f}%)', ha='center', va='bottom')
# Plot the distribution of negative sentiment for N26 in the fourth subplot
n26_cons_sentiment.plot(kind='bar', ax=axes[1, 1], color=[sentiment_custom_colors[label] for label in n26_cons_sentiment.index])
axes[1, 1].set_title('Distribution of Negative Employee Sentiment (Cons_cleaned) while Working at N26')
axes[1, 1].set_xlabel('Sentiment')
axes[1, 1].set_ylabel('Count')
axes[1, 1].tick_params(axis='x', rotation=360) # Rotate x-axis labels by 360 degrees
# Add count and percentage labels on the bar plot for N26 Cons
for index, value in enumerate(n26_cons_sentiment.values):
axes[1, 1].text(index, value, f'{value}\n({value/sum(n26_cons_sentiment.values)*100:.1f}%)', ha='center', va='bottom')
# Adjust the layout so there's no overlap
plt.tight_layout()
plt.show()
For the positive reviews, the "Pros_cleaned" chart of Deutsche Bank shows a large majority of sentiments are positive (76% or 152 comments), with a smaller proportion being neutral (21.5% or 43 comments) and very few negative (2.5% or 5 comments). Meanwhile, the "Pros_cleaned" chart of N26 also displays predominantly positive sentiments (81.5% or 163 comments), accompanied by a moderate amount of neutral (17% or 34 comments) and a minimal number of negative sentiments (1.5% or 3 comments).
For the negative side, the "Cons_cleaned" chart of Deutsche Bank presents a predominant negative sentiment (41.5% or 83 comments), followed by a substantial neutral sentiment (37.5% or 75 comments), and some positive sentiment (21% or 42 comments). On the other hand, the "Cons_cleaned" chart of N26 is slightly different to Deutsche Bank's, with neutral sentiments being the majority (35.5% or 71 comments), negative sentiments in the middle (33% or 66 comments), and positive sentiments being the least (31.5% or 63 comments).
Overall, it could safely say that N26’s employees tend to express slightly more positive sentiments compared to those at Deutsche Bank. However, the sentiment among Deutsche Bank employees appears to be more negative overall. Interestingly, while the majority of negative comments at Deutsche Bank lean towards a negative sentiment, N26 exhibits a different pattern, with a significant portion of negative comments being perceived as neutral.
To achieve my research question (how do employee reviews reflect the differences in job satisfaction and organizational culture between traditional banks (Deutsche Bank) and FinTech companies (N26)?), I aim to create a comprehensive analysis by initializing a dictionary to track the count of sentiments associated with specific keywords (salary, benefits, colleague, team, manager, leader, management, work-life balance, culture, environment, promotion, career, growth, and opportunity). The sentiments are categorized as positive, neutral, and negative with different keywords, providing insights into the sentiment distribution with various work aspects of employee experiences at both Deutsche Bank and N26.
# Keywords to track
keywords = ['salary', 'benefit', 'colleague', 'team', 'manager', 'leader', 'management', 'work life balance', 'culture', 'environment', 'promotion', 'career', 'growth', 'opportunity']
# Initialize a dictionary to hold the count of sentiments by keyword
sentiment_counts = {keyword: {'positive': 0, 'neutral': 0, 'negative': 0} for keyword in keywords}
# Function to update counts
def update_counts(text, sentiment):
for keyword in keywords:
if keyword in text.lower():
sentiment_counts[keyword][sentiment] += 1
# Analyze sentiment and update counts for Deutsche Bank
for index, row in db.iterrows():
pros_sentiment = sentiment_label(row['Pros_cleaned'])
cons_sentiment = sentiment_label(row['Cons_cleaned'])
update_counts(row['Pros_cleaned'], pros_sentiment)
update_counts(row['Cons_cleaned'], cons_sentiment)
# Plotting
fig, ax = plt.subplots(figsize=(20, 8))
# Plot data
for i, keyword in enumerate(keywords):
negative_count = sentiment_counts[keyword]['negative']
neutral_count = sentiment_counts[keyword]['neutral']
positive_count = sentiment_counts[keyword]['positive']
total_count = negative_count + neutral_count + positive_count
negative_percentage = (negative_count / total_count) * 100
neutral_percentage = (neutral_count / total_count) * 100
positive_percentage = (positive_count / total_count) * 100
ax.bar(i - 0.2, negative_count, width=0.2, color='firebrick', align='center', label='negative' if i == 0 else "")
ax.bar(i, neutral_count, width=0.2, color='orange', align='center', label='neutral' if i == 0 else "")
ax.bar(i + 0.2, positive_count, width=0.2, color='green', align='center', label='positive' if i == 0 else "")
ax.text(i - 0.2, negative_count, f'{negative_percentage:.1f}%', ha='center', va='bottom')
ax.text(i, neutral_count, f'{neutral_percentage:.1f}%', ha='center', va='top')
ax.text(i + 0.2, positive_count, f'{positive_percentage:.1f}%', ha='center', va='bottom')
# Labels and titles
ax.set_ylabel('Counts')
ax.set_title('Employee Sentiment Distribution with Various Aspects while Working at Deutsche Bank')
ax.set_xticks(range(len(keywords)))
ax.set_xticklabels(keywords, rotation=45, ha='right')
ax.legend()
plt.show()
# Keywords to track
keywords = ['salary', 'benefit', 'colleague', 'team', 'manager', 'leader', 'management', 'work life balance', 'culture', 'environment', 'promotion', 'career', 'growth', 'opportunity']
# Initialize a dictionary to hold the count of sentiments by keyword
sentiment_counts = {keyword: {'positive': 0, 'neutral': 0, 'negative': 0} for keyword in keywords}
# Function to update counts
def update_counts(text, sentiment):
for keyword in keywords:
if keyword in text.lower():
sentiment_counts[keyword][sentiment] += 1
# Analyze sentiment and update counts for Deutsche Bank
for index, row in n26.iterrows():
pros_sentiment = sentiment_label(row['Pros_cleaned'])
cons_sentiment = sentiment_label(row['Cons_cleaned'])
update_counts(row['Pros_cleaned'], pros_sentiment)
update_counts(row['Cons_cleaned'], cons_sentiment)
# Plotting
fig, ax = plt.subplots(figsize=(20, 8))
# Plot data
for i, keyword in enumerate(keywords):
negative_count = sentiment_counts[keyword]['negative']
neutral_count = sentiment_counts[keyword]['neutral']
positive_count = sentiment_counts[keyword]['positive']
total_count = negative_count + neutral_count + positive_count
negative_percentage = (negative_count / total_count) * 100
neutral_percentage = (neutral_count / total_count) * 100
positive_percentage = (positive_count / total_count) * 100
ax.bar(i - 0.2, negative_count, width=0.2, color='firebrick', align='center', label='negative' if i == 0 else "")
ax.bar(i, neutral_count, width=0.2, color='orange', align='center', label='neutral' if i == 0 else "")
ax.bar(i + 0.2, positive_count, width=0.2, color='green', align='center', label='positive' if i == 0 else "")
ax.text(i - 0.2, negative_count, f'{negative_percentage:.1f}%', ha='center', va='bottom')
ax.text(i, neutral_count, f'{neutral_percentage:.1f}%', ha='center', va='top')
ax.text(i + 0.2, positive_count, f'{positive_percentage:.1f}%', ha='center', va='bottom')
# Labels and titles
ax.set_ylabel('Counts')
ax.set_title('Employee Sentiment Distribution with Various Aspects while Working at N26')
ax.set_xticks(range(len(keywords)))
ax.set_xticklabels(keywords, rotation=45, ha='right')
ax.legend()
plt.show()
To compare employee sentiment between traditional banks (Deutsche Bank) and FinTech companies (N26), it is essential to consider various work aspects that may differ between the two types of organizations as follows:
(1) Compensation (‘salary’ and ‘benefit’): At Deutsche Bank, the sentiment towards ‘benefit’ leans predominantly positive, with approximately 55.6% of comments expressing satisfaction. However, the sentiment spread for ‘salary’ appears more balanced, with around 37.5% of comments being positive and negative, suggesting potential variations based on factors such as work experience and job positions. In contrast, N26 employees overwhelmingly express positive sentiments regarding both ‘salary’ (approximately 52.6%) and ‘benefits’ (around 70.4%). This indicates a notable difference in employee perception between the two organizations, with N26's compensation structures being well-received and potentially contributing to a positive organizational culture. The higher satisfaction levels at N26 may reflect a strategic emphasis on competitive and appealing remuneration packages to attract and retain talent in the rapidly evolving FinTech industry.
(2) Collaboration (‘colleague’ and ‘team’): Both companies exhibit a notably strong positive sentiment towards both the ‘colleague’ and ‘team’ aspects, with approximately 81.8% and 83.3% of comments expressing satisfaction with ‘colleagues’, and around 70.6% and 70.1% showing positivity towards ‘teamwork’ at Deutsche Bank and N26, respectively. These findings reflect a cohesive and supportive workplace culture characterized by peer support, teamwork, and collaboration in both organizations. However, it is noticeable that while the sentiment towards ‘team’ remains predominantly positive, there are some amount of negative and neutral sentiments across both companies. This nuanced sentiment distribution may indicate potential variations in individual relationships or experiences among staff members within the team dynamics.
(3) Management and Leadership (‘manager’, ‘leader’, and ‘management’): Both companies exhibit a predominantly positive sentiment towards the ‘manager’ and ‘leader’ aspects, with approximately 55.6% and 78.6% of comments expressing satisfaction with ‘manager’, and around 50% and 56.2% showing positivity towards ‘leader’ at Deutsche Bank and N26, respectively. However, there is a notable presence of negative sentiment towards ‘manager’, accounting for around 33.3% at Deutsche Bank, and towards ‘leader’, with 25% at N26. This suggests **a diverse employee experience that may correlate with different management levels or styles within each organization. While the majority of employees may express satisfaction with their leaders, the presence of negative feedback highlights the need for potential improvements, such as refining leadership practices or addressing specific concerns raised by employees. In contrast, N26 displays a higher level of negative sentiment, with approximately 43.9%* in ‘management’ aspect compared to Deutsche Bank. This disparity may indicate *challenges or dissatisfaction among N26 employees regarding leadership practices or organizational management structures.*** It suggests areas where N26 could focus on implementing strategies, such as fostering open communication channels between management and employees to foster a more positive and supportive work environment that aligns with the innovative and dynamic culture typically associated with FinTech companies.
(4) Work-Life Balance (‘work life balance’): Both companies tend to exhibit a strong positive sentiment in the ‘work life balance’ aspect, with interestingly Deutsche Bank showing a significantly higher level of positivity compared to N26, around 72.2% and 66.7%, respectively. This suggests that employees at Deutsche Bank may perceive the organization as placing a greater emphasis on promoting and maintaining a healthy work-life balance compared to their counterparts at N26. The higher level of positivity in this aspect at Deutsche Bank may indicate that the traditional bank has implemented effective policies, practices, and initiatives to support employees in achieving a satisfactory balance between their professional and personal lives.
(5) Company Culture (‘culture’ and ‘environment’): Employees of both companies perceive significantly positive sentiment in the ‘culture’ and ‘environment’, with approximately 81% and 66.7% of comments expressing satisfaction with ‘culture’, and around 74.1% and 76.6% showing positivity towards ‘environment’ at Deutsche Bank and N26, respectively. This indicates that employees feel supported, valued, and comfortable in their respective work settings at both organizations.
(6) Career Progression (‘promotion’, ‘career’, ‘growth’, and ‘opportunity’): At Deutsche Bank, the sentiment regarding career opportunities seems more mixed, with a considerable number of employees reporting neutral experiences. The distribution of sentiments is fairly even across positive, neutral, and negative, which suggests that experiences with career opportunities may vary widely among employees. For instance, while the ‘career’ and ‘opportunity’ aspects show positivity in sentiment, around 39.1% and 50%, respectively, the sentiment towards ‘growth’ appears to be neutral, with approximately 57.1%. Notably, the aspect of ‘promotion’ exhibits a significantly high level of negativity, around 64.3%. This could indicate that while employees at Deutsche Bank may perceive opportunities for career advancement, there could be perceived barriers or inconsistencies in the process or availability of promotions, leading to dissatisfaction or frustration among some employees. For N26, there is a significantly higher proportion of positive sentiments related to the ‘career’, ‘growth', and ‘opportunity’ aspects, with approximately 50%, 66.7%, and 83.9%, respectively, except ‘promotion’, which shows a higher prevalence of negative sentiment, at around 44.4%. The majority of employees have shared positive reviews, indicating a strong culture of promoting career growth and providing opportunities within the FinTech company. The low percentage of negative sentiment might suggest that barriers to career advancement are less common, or that such issues are not as impactful on employees' perceptions of their career opportunities at N26. Overall, N26 appears to have a more favorable employee perception of career opportunities when compared to Deutsche Bank. The high positive sentiment at N26 could be reflective of a dynamic, growth-oriented culture often found in FinTech startups, which may provide more agility and openness to employee advancement. In contrast, the more traditional and possibly hierarchical structure at Deutsche Bank might contribute to the mixed feelings, as progression may be seen as more rigid or conventional.
In summary, my analysis of employee reviews provides clear differences in job satisfaction and organizational culture between Deutsche Bank, a traditional bank, and N26, a FinTech company. While both exhibit strengths in collaboration, work-life balance, and company culture, distinct perceptions emerge in compensation and career progression. N26 employees express predominantly positivity towards salary, benefits, and career opportunities, reflecting a robust emphasis on competitive compensation and a growth-oriented culture. In contrast, while Deutsche Bank receives favorable feedback on benefits, sentiments towards salary are more nuanced, suggesting potential variations in compensation satisfaction. Additionally, perceptions of management and leadership differ, with Deutsche Bank generally exhibiting higher satisfaction levels. Conversely, negative sentiment towards management is more prevalent at N26, suggesting potential challenges in leadership practices. Overall, these insights underscore the imperative for traditional banks to adapt to changing employee expectations and industry dynamics to enhance job satisfaction and foster a positive organizational culture, aligning with the practices seen in innovative FinTech companies like N26.
However, I acknowledge that these findings may not completely represent the experiences of all employees within traditional banks and FinTech companies. Therefore, it is essential to consider the following limitations.
(1) Bias in Reviews: Glassdoor reviews may be biased, as extreme opinions are more likely to be expressed, potentially skewing the analysis.
(2) Temporal Considerations: Employee sentiments can change over time due to various factors. Analyzing reviews at a single point may not capture these variations adequately.
(3) Data Quality: Data reliability from Glassdoor reviews varies, and automated sentiment analysis may not always accurately capture nuances.
(4) Generalizability: Findings from Deutsche Bank and N26 reviews may not apply to all traditional banks or FinTech firms in Germany due to factors like organizational culture and location.
(5) Limited Scope: Reviews from Glassdoor may not capture all work aspects of the employee experience such as attitudes toward innovation and technology adoption, including diversity and inclusion efforts.
Addressing these limitations will lead to a more comprehensive assessment of the project's constraints and considerations.
To conduct a more comprehensive comparative analysis, it is valuable to explore additional methods for further research and to consider the potential applications of the findings.
(1) Further Comparative Analysis: Including more traditional banks and FinTech firms in diverse regions or similar market scales can provide a broader perspective on job satisfaction and organizational culture in Germany's banking sector.
(2) Longitudinal Study: Tracking changes in employee sentiment over time within Deutsche Bank and N26 could reveal trends or patterns in job satisfaction and organizational culture evolution.
(3) Impact of External Factors: Exploring how regulations, market trends, and technological advancements interact with job satisfaction and organizational culture can enhance the interpretation of findings.
(4) Employee Retention and Performance: Analyzing the relationship between employee sentiment, retention rates, and performance metrics could provide actionable insights for human resource management.
(5) Cross-Cultural Analysis: Conducting cross-cultural analyses on job satisfaction between traditional banks and FinTech companies can assess the impact of cultural factors across diverse contexts.
Exploring these methods can provide valuable insights into job satisfaction, organizational culture, and employee sentiment within the banking sector.
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